Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems | Paper Citation Record

Observation e8b47a89-cf91-41fd-a6be-0e7c15879f16 · outbound

This paper cites Evolutionary co-optimization of rule shape and fuzzi- ness in rule-based machine learning,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Evolutionary co-optimization of rule shape and fuzzi- ness in rule-based machine learning,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.577323Z

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Observation 5604e1a8-b77f-4d72-b025-69a53e2c9460 · outbound

This paper cites an unresolved cited work.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Unresolved cited work

Reference 2

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Observation eace4d7b-a869-4e20-890a-452107fac94d · outbound

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Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Unresolved cited work

Reference 3

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Observation 5ce17d02-54b8-4a8b-a7c5-3d1712f8b2e5 · outbound

This paper cites XCS classifier system reliably evolves accurate, complete, and minimal representations for Boolean functions,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems XCS classifier system reliably evolves accurate, complete, and minimal representations for Boolean functions,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.529408Z

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Observation 1ce696b3-f35a-468a-835e-557f6f89b379 · outbound

This paper cites Autoencoding with a classifier system,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Autoencoding with a classifier system,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.513863Z

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Observation dafeb9ae-5a3f-4470-a352-15fb982a1bf6 · outbound

This paper cites Accuracy-based learning classifier systems: Models, analysis and applications to classification tasks,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Accuracy-based learning classifier systems: Models, analysis and applications to classification tasks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.498071Z

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Observation 30432eb3-3088-4974-82a8-5336bed6c976 · outbound

This paper cites Survival-LCS: A rule-based machine learning approach to survival analysis,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Survival-LCS: A rule-based machine learning approach to survival analysis,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.478176Z

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Observation d08d5682-820c-4017-a7ad-cd91713ebae5 · outbound

This paper cites Can the same rule representation change its matching area? enhancing representation in XCS for continuous space by probability distribution in multiple dimension,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Can the same rule representation change its matching area? enhancing representation in XCS for continuous space by probability distribution in multiple dimension,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.457553Z

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Observation 5828710a-0a9a-416a-b3d4-322395612a7e · outbound

This paper cites Mining oblique data with XCS,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Mining oblique data with XCS,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.441440Z

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Observation f9fc1a6b-3c68-455e-a52d-3cab7411a4ed · outbound

This paper cites Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.421297Z

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Observation 972e7e01-4c20-4fd2-8cec-4204f3d3394e · outbound

This paper cites Classifier conditions using gene expression program- ming,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Classifier conditions using gene expression program- ming,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.405509Z

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Observation afece09d-57bf-441d-9177-f2b574242d45 · outbound

This paper cites Accuracy-based neuro and neuro-fuzzy classifier systems,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Accuracy-based neuro and neuro-fuzzy classifier systems,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.385494Z

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Observation 7a5121c0-fd44-461c-ae24-0f9dc24735fe · outbound

This paper cites Fuzzy-XCS: A michigan genetic fuzzy system,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Fuzzy-XCS: A michigan genetic fuzzy system,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.359129Z

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Observation c627466f-f4c7-4ab5-86f4-c6c8a5121133 · outbound

This paper cites Towards final rule set reduction in XCS: A fuzzy representation approach,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Towards final rule set reduction in XCS: A fuzzy representation approach,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.344301Z

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Observation 11f9280f-cae3-4a30-b884-452c5c3fd444 · outbound

This paper cites Fuzzy-UCS revisited: Self-adaptation of rule representations in michigan-style learning fuzzy- classifier systems,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Fuzzy-UCS revisited: Self-adaptation of rule representations in michigan-style learning fuzzy- classifier systems,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.323349Z

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Observation 0ea50a8e-f3e6-4ecd-98c2-e8b355946550 · outbound

This paper cites Fuzzy-UCS: A michigan-style learning fuzzy-classifier system for supervised learning,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Fuzzy-UCS: A michigan-style learning fuzzy-classifier system for supervised learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.310184Z

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Observation ead96d00-a73a-49e9-952f-07b9474469b5 · outbound

This paper cites Generalization in the XCS classifier system,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Generalization in the XCS classifier system,

Reference 17

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T11:06:24.200057Z

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Observation 776c22ef-4976-4b4a-9d7b-c7c2f9f95222 · outbound

This paper cites Extracting both generalized and special- ized knowledge by XCS using attribute tracking and feedback,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Extracting both generalized and special- ized knowledge by XCS using attribute tracking and feedback,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.296281Z

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Observation 87b6efd6-f1a4-43d1-a3cc-fe0d60d39019 · outbound

This paper cites Bayesian inference for a flexible class of bivariate beta distributions,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Bayesian inference for a flexible class of bivariate beta distributions,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.275870Z

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Observation 03154a98-689c-42d0-9d0b-ac101e587cd1 · outbound

This paper cites Get real! XCS with continuous-valued inputs,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Get real! XCS with continuous-valued inputs,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.261289Z

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Observation 82de5303-1eb2-497d-88d5-f9826d98a98e · outbound

This paper cites For real! XCS with continuous-valued inputs,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems For real! XCS with continuous-valued inputs,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.244959Z

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Observation b5d83258-2cb0-47dc-bacc-9d62bc1ca293 · outbound

This paper cites Be real! XCS with continuous-valued inputs,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Be real! XCS with continuous-valued inputs,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.228957Z

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Observation 6c01177e-dbab-4f1c-b47d-153e9f0ca2d1 · outbound

This paper cites Using convex hulls to represent classifier conditions,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Using convex hulls to represent classifier conditions,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.212933Z

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Observation 772509f7-fd3c-4b50-bc6e-a2e2b9597731 · outbound

This paper cites Solving social media text classification problems using code fragment-based XCSR,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Solving social media text classification problems using code fragment-based XCSR,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.193428Z

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Observation 1625bbf5-2af0-4b46-8a63-bb66c480f794 · outbound

This paper cites Beta distribution based XCS classifier system,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Beta distribution based XCS classifier system,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.175496Z

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Observation 8cec5ec7-5ffc-42c8-a3b4-83018ef727e2 · outbound

This paper cites Coevolving different knowledge represen- tations with fine-grained parallel learning classifier systems,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Coevolving different knowledge represen- tations with fine-grained parallel learning classifier systems,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.148735Z

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Observation 24ddd3c5-5267-480d-9570-21078b68d723 · outbound

This paper cites Evolving multiple discretizations with adaptive intervals for a pittsburgh rule-based learning classifier system,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Evolving multiple discretizations with adaptive intervals for a pittsburgh rule-based learning classifier system,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.127912Z

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Observation 2c98e4e8-7a57-4ee3-b42c-20257851fe31 · outbound

This paper cites Approximate versus linguistic representation in Fuzzy-UCS,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Approximate versus linguistic representation in Fuzzy-UCS,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.110187Z

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Observation dd243787-988e-4683-b294-96429a5c8fbb · outbound

This paper cites XCS with weight-based matching in V AE latent space and additional learning of high- dimensional data,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems XCS with weight-based matching in V AE latent space and additional learning of high- dimensional data,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.084947Z

Source-reported events for the cited work

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Observation d1eb1ede-3336-48b6-a144-ac307a6d084f · outbound

This paper cites Function approximation with XCS: Hyperellipsoidal conditions, recursive least squares, and compaction,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Function approximation with XCS: Hyperellipsoidal conditions, recursive least squares, and compaction,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.069597Z

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Observation baa16ffb-036e-4e0a-bb14-ba382ae2f74f · outbound

This paper cites Classifier fitness based on accuracy,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Classifier fitness based on accuracy,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.048360Z

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Observation 2b7796d6-1d77-4c0e-a331-e5cc5806597c · outbound

This paper cites Ferreira, Gene expression programming: mathematical modeling by an artificial intelligence.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Ferreira, Gene expression programming: mathematical modeling by an artificial intelligence

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.034240Z

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Observation 254c40f5-983c-4bf3-9481-183afab69210 · outbound

This paper cites Reusing building blocks of extracted knowledge to solve complex, large-scale boolean problems,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Reusing building blocks of extracted knowledge to solve complex, large-scale boolean problems,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:25.016359Z

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Observation 5e203d82-7a2b-46cb-a12c-86c7224262e1 · outbound

This paper cites Comparison between fuzzy and interval partitions in evolutionary multiobjective design of rule-based classifica- tion systems,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Comparison between fuzzy and interval partitions in evolutionary multiobjective design of rule-based classifica- tion systems,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.997839Z

Source-reported events for the cited work

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Observation 3213f5ba-47bc-4992-a564-de3dd67d7574 · outbound

This paper cites Fuzzy knowledge representation study for incremental learning in data streams and classification problems,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Fuzzy knowledge representation study for incremental learning in data streams and classification problems,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.976986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9026925d-6741-4095-9647-5d3ab693c6bb · outbound

This paper cites The beta fuzzy system: approximation of stan- dard membership functions,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems The beta fuzzy system: approximation of stan- dard membership functions,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.961899Z

Source-reported events for the cited work

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Observation c82f8f01-6930-4816-bebd-f2e06d3dde7a · outbound

This paper cites Beta fuzzy logic systems: approximation properties in the mimo case,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Beta fuzzy logic systems: approximation properties in the mimo case,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.943274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3fa39612-be37-455f-ae47-3faf4184468d · outbound

This paper cites A hybrid learning algorithm for evolving flexible beta basis function neural tree model,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems A hybrid learning algorithm for evolving flexible beta basis function neural tree model,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.928100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e254d135-e59b-4a50-be0f-0b4411a4eecc · outbound

This paper cites A beta basis function interval type-2 fuzzy neural network for time series applications,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems A beta basis function interval type-2 fuzzy neural network for time series applications,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.907295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bd92d5e5-21bc-4c6c-a1db-3fdea0169450 · outbound

This paper cites Rule weight specification in fuzzy rule- based classification systems,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Rule weight specification in fuzzy rule- based classification systems,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.879081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 74d7382f-9852-4746-acdd-fe1c0a94d825 · outbound

This paper cites Toward a theory of generalization and learning in XCS,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Toward a theory of generalization and learning in XCS,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.865138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7ec800b1-5d81-43ed-894b-aaa9edc87ad7 · outbound

This paper cites Absumption based on overgenerality and condition-clustering based specialization for XCS with continuous-valued inputs,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Absumption based on overgenerality and condition-clustering based specialization for XCS with continuous-valued inputs,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.847749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fddd7268-ab67-4fdf-8a58-399832a81efb · outbound

This paper cites Learning optimality theory for accuracy- based learning classifier systems,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Learning optimality theory for accuracy- based learning classifier systems,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.821427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 289e1942-4d16-402a-b8e7-4c9adf90666d · outbound

This paper cites To handle real valued input in XCS: using fuzzy hyper-trapezoidal membership in classifier condition,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems To handle real valued input in XCS: using fuzzy hyper-trapezoidal membership in classifier condition,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.799821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ea2d7f44-de88-4571-a358-174cb991f26c · outbound

This paper cites Absumption to complement sub- sumption in learning classifier systems,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Absumption to complement sub- sumption in learning classifier systems,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.770761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3ff0c477-34f3-4af7-855e-5d1f29a19ee5 · outbound

This paper cites Ockham’s razor in memetic computing: three stage optimal memetic exploration,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Ockham’s razor in memetic computing: three stage optimal memetic exploration,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.751368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 21c8493c-9231-4af1-bf9e-66a3d08f0e59 · outbound

This paper cites UCI machine learning repository,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems UCI machine learning repository,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T11:06:23.668691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52340c9f-7f4e-4f09-83da-5ede2503d7be · outbound

This paper cites Minimum rule-repair algorithm for supervised learning classifier systems on real-valued classification tasks,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Minimum rule-repair algorithm for supervised learning classifier systems on real-valued classification tasks,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.720355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 12cdeb67-0f55-435d-8e98-4158f7deb432 · outbound

This paper cites Strength-based learning classifier systems revisited: Effective rule evolution in supervised classification tasks,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Strength-based learning classifier systems revisited: Effective rule evolution in supervised classification tasks,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.679213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5bf1bd84-caaf-4092-ba59-a1fec336cf4f · outbound

This paper cites ExSTraCS 2.0: description and evaluation of a scalable learning classifier system,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems ExSTraCS 2.0: description and evaluation of a scalable learning classifier system,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.662539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 449654f4-654e-42af-a4c3-fb14e6f7b1b6 · outbound

This paper cites Classifiers that approximate functions,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Classifiers that approximate functions,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.644583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6be87ad5-4d52-4a40-98db-366c54e65ff9 · outbound

This paper cites Analysis and im- provement of fitness exploitation in xcs: Bounding models, tournament selection, and bilateral accuracy,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Analysis and im- provement of fitness exploitation in xcs: Bounding models, tournament selection, and bilateral accuracy,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.621198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9bf37de6-a2f8-472b-a663-2f4f4eed7694 · outbound

This paper cites Classification of date fruits into genetic varieties using image analysis,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Classification of date fruits into genetic varieties using image analysis,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.585206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0dee5e0c-fd59-4c90-8466-0dfb59ee3a54 · outbound

This paper cites Conversion predictors of clinically isolated syndrome to multiple sclerosis in mexican patients: a prospective study,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Conversion predictors of clinically isolated syndrome to multiple sclerosis in mexican patients: a prospective study,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.561953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 51708dd0-f3cf-42bd-b5d2-6ef5cb5e0ab9 · outbound

This paper cites National poll on healthy aging (NPHA), [united states], april 2017.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems National poll on healthy aging (NPHA), [united states], april 2017

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.543214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1d6b84a0-d3ef-4525-871d-20055aa80c79 · outbound

This paper cites Classification and analysis of pistachio species with pre-trained deep learning models,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Classification and analysis of pistachio species with pre-trained deep learning models,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.525917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c591751c-feeb-471b-80c0-f2f05eb68f8e · outbound

This paper cites The use of machine learning methods in classification of pumpkin seeds (cucurbita pepo l.),.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems The use of machine learning methods in classification of pumpkin seeds (cucurbita pepo l.),

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.510856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation abec180f-60f6-435a-a94a-f1be02c0d4a9 · outbound

This paper cites Classification of raisin grains using machine vision and artificial intelligence methods,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Classification of raisin grains using machine vision and artificial intelligence methods,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.488349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ba5e76c4-b8ab-4012-b6a4-95d49f0436f1 · outbound

This paper cites Julia: A fresh approach to numerical computing,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Julia: A fresh approach to numerical computing,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.460147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f4da471f-4569-495f-9cc6-a9cde1e419df · outbound

This paper cites Generic approaches for parallel rule matching in learning classifier systems,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Generic approaches for parallel rule matching in learning classifier systems,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.430448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b00ddbe5-e875-41af-8726-03750e185e14 · outbound

This paper cites Model-based explanations of concept drift,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Model-based explanations of concept drift,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.403548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9129f791-71f7-4cd8-9f30-c505b46760f6 · outbound

This paper cites Fuzzy logic based control for autonomous mobile robot navigation,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Fuzzy logic based control for autonomous mobile robot navigation,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.365070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e01e2e9d-798c-48ff-a544-1ccf6c5b15b4 · outbound

This paper cites Explainable artificial intelligence (xai)—from theory to methods and applications,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Explainable artificial intelligence (xai)—from theory to methods and applications,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.332662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6fdad59c-6613-45c3-ae86-f6f68f90db94 · outbound

This paper cites Mechanisms to alleviate over- generalization in XCS for continuous-valued input spaces,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Mechanisms to alleviate over- generalization in XCS for continuous-valued input spaces,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.315096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation dcc49c62-f3cd-4f7e-962e-181f93c05d3e · outbound

This paper cites SupRB in the context of rule-based machine learning methods: A comparative study,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems SupRB in the context of rule-based machine learning methods: A comparative study,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.294994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 843aabcb-4dfb-4828-9306-c2d2445d44bf · outbound

This paper cites A variable-length fuzzy set representation for learning fuzzy-classifier systems,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems A variable-length fuzzy set representation for learning fuzzy-classifier systems,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.274091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 10a20c8b-5843-461e-8bfc-53f7f1c06d69 · outbound

This paper cites Prediction and outlier detection in clas- sification problems,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Prediction and outlier detection in clas- sification problems,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.259381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e049c2d6-239f-4ad7-9673-9b02f8b95b79 · outbound

This paper cites Rapid rule compaction strategies for global knowledge discovery in a supervised learning classifier system,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems Rapid rule compaction strategies for global knowledge discovery in a supervised learning classifier system,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.236048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 06849b13-320b-4842-b592-817884b19914 · outbound

This paper cites A comparison of learning classifier systems’ rule compaction algorithms for knowledge visualization,.

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems A comparison of learning classifier systems’ rule compaction algorithms for knowledge visualization,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:06:24.221030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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