Paper Citation Record · LEDGER

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform

As of 16 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 1 inbound Pith citation observation for arXiv:1908.05376.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.05376 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:20:02.785309Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T04:45:38.684903Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-20T04:53:05.185020Z

Reference resolution

24 of 24 outbound references displayed

verified exact0
verified fuzzy15
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2f8acba2-64cc-4177-8545-840f19bb170c · outbound

This paper cites Katsov, Introduction to Algorithmic Marketing: Artiﬁcial Intelligence for Marketing Operations.

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform Katsov, Introduction to Algorithmic Marketing: Artiﬁcial Intelligence for Marketing Operations

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:20:03.050224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f2ad41b2-ef96-4389-a608-adeb300c38a1 · outbound

This paper cites A review of feature selection methods on synthetic data,.

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform A review of feature selection methods on synthetic data,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:20:03.039309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b1e5543c-1ff5-4bbd-8e2d-d15bdceb1e60 · outbound

This paper cites A survey on feature selection methods,.

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform A survey on feature selection methods,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-14T13:20:02.705022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95f453ea-b363-4d55-b185-0ea4159e4fb5 · outbound

This paper cites Feature selection for classiﬁcation: A review,.

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform Feature selection for classiﬁcation: A review,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:20:03.019858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 12ca5c0a-7954-443c-befe-92c6a499edfe · outbound

This paper cites The best two independent measurements are not the two best,.

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform The best two independent measurements are not the two best,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:20:03.007657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 788eba24-4736-4c74-a809-4e50a00a6426 · outbound

This paper cites Feature selection based on mu- tual information: criteria of max-dependency, max-relevance, and min- redundancy,.

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform Feature selection based on mu- tual information: criteria of max-dependency, max-relevance, and min- redundancy,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:20:02.995206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f2dbb076-c632-4c5e-a68c-6e3df9351541 · outbound

This paper cites Normalized mutual information feature selection,.

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform Normalized mutual information feature selection,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:20:02.982974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0c828520-72f9-4298-8c29-34fa59ab20f4 · outbound

This paper cites An improved maximum relevance and minimum redundancy feature selection algorithm based on normalized mutual information,.

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform An improved maximum relevance and minimum redundancy feature selection algorithm based on normalized mutual information,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:20:02.971212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6826a2fb-438e-49a1-962f-a4c3d088a525 · outbound

This paper cites Conditional likelihood maximisation: a unifying framework for information theoretic feature selection,.

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform Conditional likelihood maximisation: a unifying framework for information theoretic feature selection,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-14T13:20:02.728860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f733308-3fe3-46af-87a5-e6d1db5cd072 · outbound

This paper cites Fast-mrmr: Fast minimum redundancy maximum relevance algorithm for high- dimensional big data,.

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform Fast-mrmr: Fast minimum redundancy maximum relevance algorithm for high- dimensional big data,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:20:02.952825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b27c5f11-c4b0-4e8b-9700-c388443d8654 · outbound

This paper cites Minimum redundancy feature selection from microarray gene expression data,.

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform Minimum redundancy feature selection from microarray gene expression data,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-14T13:20:02.736712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6538a24c-296f-4b50-a44c-d47626372bd0 · outbound

This paper cites A new maximum relevance- minimum multicollinearity (mrmmc) method for feature selection and ranking,.

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform A new maximum relevance- minimum multicollinearity (mrmmc) method for feature selection and ranking,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:20:02.934591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a66fc134-a456-4587-ba77-37f9c0d38f4d · outbound

This paper cites Random forests,.

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform Random forests,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-14T13:20:02.744012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e24d5ab-77e1-4156-af9d-48740a892d9f · outbound

This paper cites Churn prediction in telecom using random forest and pso based data balancing in combination with vari- ous feature selection strategies,.

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform Churn prediction in telecom using random forest and pso based data balancing in combination with vari- ous feature selection strategies,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:20:02.916275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 55990a69-8a32-4a84-8047-53239d1e0b67 · outbound

This paper cites The randomized de- pendence coefﬁcient,.

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform The randomized de- pendence coefﬁcient,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:20:02.904893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 93cc1771-935b-4d9d-9689-89ffcede389a · outbound

This paper cites Stochastic gradient boosting,.

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform Stochastic gradient boosting,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:20:02.892467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fbb5bd65-f5e9-43bc-9bbd-32c97935e1cb · outbound

This paper cites Xgboost: A scalable tree boosting system,.

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform Xgboost: A scalable tree boosting system,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-14T13:20:02.759645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d64855e5-4155-4a50-8060-70125a674ef4 · outbound

This paper cites A comparison of random forest and its gini importance with standard chemometric methods for the fea- ture selection and classiﬁcation of spectral data,.

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform A comparison of random forest and its gini importance with standard chemometric methods for the fea- ture selection and classiﬁcation of spectral data,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:20:02.872756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1f9badd7-3dc6-46ac-855b-33029cd4c34c · outbound

This paper cites Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation,.

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-14T13:20:02.766861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e9599da-342a-477f-8166-172ccdcf09a7 · outbound

This paper cites An empirical study of the naive bayes classiﬁer,.

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform An empirical study of the naive bayes classiﬁer,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-14T13:20:02.770368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0545af6-ad44-4d90-a518-b003867dc780 · outbound

This paper cites an unresolved cited work.

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform Unresolved cited work

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-14T13:20:02.773918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5206ecc-19fc-4331-99cf-46689f096eaf · outbound

This paper cites A study of cross-validation and bootstrap for accuracy estimation and model selection,.

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform A study of cross-validation and bootstrap for accuracy estimation and model selection,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:20:02.837949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b44c3921-1b67-4591-8e9b-9cc33aa65db7 · outbound

This paper cites Smoothing by spline functions,.

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform Smoothing by spline functions,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:20:02.824981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f4096fad-bd04-4db2-b9a7-b7c65c5db3ab · outbound

This paper cites Scikit-learn: Machine learning in Python,.

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform Scikit-learn: Machine learning in Python,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-14T13:20:02.785309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation c9b03555-ead5-46e3-8b74-08d195bdb803 · inbound

Equalized Coverage in Motion Control Performance Prediction for Self-Adaptive Road Vehicles cites this paper.

Equalized Coverage in Motion Control Performance Prediction for Self-Adaptive Road Vehicles Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-20T04:53:05.189060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.