Paper Citation Record · LEDGER

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach

As of 22 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2506.20359.

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

pith.paper-citation-record.v1
2506.20359 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:54:59.294142Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

verified exact4
verified fuzzy16
unresolved9
parse uncertain0
malformed identifier1
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 71458b19-6525-4a45-8ea2-18b196a5df2f · outbound

This paper cites Trajectory analysis: An overview,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach Trajectory analysis: An overview,

Reference 1

Resolution: verified exact; doi, observed 2026-08-06T22:54:59.657199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6927683d-007d-41f8-b461-23450be342ef · outbound

This paper cites Ptrail — a python package for parallel trajectory data preprocessing,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach Ptrail — a python package for parallel trajectory data preprocessing,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T22:54:57.299263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0193de1e-015b-4c7d-a9d1-4c094543a3bc · outbound

This paper cites A Novel Multilevel Taxonomical Approach for Describing High-Dimensional Unlabeled Movement Data.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach A Novel Multilevel Taxonomical Approach for Describing High-Dimensional Unlabeled Movement Data

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T22:54:57.420777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdf71994-8c3a-4918-b035-f90a94a051d6 · outbound

This paper cites A review of feature selection methods based on meta-heuristic algorithms,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach A review of feature selection methods based on meta-heuristic algorithms,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:01.592974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5d25f7f1-0862-4bb8-9cdb-03fd6c8efdce · outbound

This paper cites Feature selection in machine learning: Methods and comparison,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach Feature selection in machine learning: Methods and comparison,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T22:54:57.622888Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 927244c2-99f8-4ef3-a1fe-025d5d2eaad8 · outbound

This paper cites A survey on big data for trajectory analytics,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach A survey on big data for trajectory analytics,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:01.582926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2e411f5b-3776-4c3e-b419-19caf3a983f3 · outbound

This paper cites Enhancing global mar- itime traffic network forecasting with gravity-inspired deep learning models,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach Enhancing global mar- itime traffic network forecasting with gravity-inspired deep learning models,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T22:54:57.769850Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4acc515a-b055-4f76-9c51-985ce4bf8b4e · outbound

This paper cites Multi-path long-term vessel trajectories forecasting with probabilistic feature fusion for problem shifting,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach Multi-path long-term vessel trajectories forecasting with probabilistic feature fusion for problem shifting,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T22:54:57.821698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59c64cee-4240-471c-98d2-f742a98dc29f · outbound

This paper cites A semi-supervised method- ology for fishing activity detection using the geometry behind the trajectory of mul- tiple vessels,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach A semi-supervised method- ology for fishing activity detection using the geometry behind the trajectory of mul- tiple vessels,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:01.563288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d1cbca3a-bd53-4887-9f2d-dd389db84326 · outbound

This paper cites A study on the geometric and kine- matic descriptors of trajectories in the classification of ship types,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach A study on the geometric and kine- matic descriptors of trajectories in the classification of ship types,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:01.552861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1bb4fc0f-435a-4840-992c-cd6369cd3967 · outbound

This paper cites Assessing com- pression algorithms to improve the efficiency of clustering analysis on ais vessel trajectories,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach Assessing com- pression algorithms to improve the efficiency of clustering analysis on ais vessel trajectories,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:01.543304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5e3397e5-1265-433a-87de-054793b18f1f · outbound

This paper cites Uncovering vessel movement patterns from ais data with graph evolution analysis,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach Uncovering vessel movement patterns from ais data with graph evolution analysis,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:01.533803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation dad55a6c-28f1-46ca-afa4-db5f91ef6ebc · outbound

This paper cites Un- derstanding evolution of maritime networks from automatic identification system data,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach Un- derstanding evolution of maritime networks from automatic identification system data,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:01.525092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8ac69a70-b922-49b0-846d-81ca5026b816 · outbound

This paper cites Challenges in vessel behavior and anomaly detection: From classical machine learning to deep learning,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach Challenges in vessel behavior and anomaly detection: From classical machine learning to deep learning,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T22:54:58.192599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd2cbee7-5d1a-4f08-ba58-3c2af49d8b08 · outbound

This paper cites A trajectory scoring tool for local anomaly detection in maritime traffic using visual analytics,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach A trajectory scoring tool for local anomaly detection in maritime traffic using visual analytics,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T22:54:58.238190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9622ce8-5176-4d33-be53-4cb229129c11 · outbound

This paper cites A dash- board tool for mobility data mining preprocessing tasks,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach A dash- board tool for mobility data mining preprocessing tasks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:01.507235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f4bc51d0-a29e-4ce4-8df1-474ebcd4bd21 · outbound

This paper cites Vessel pattern recognition using trajectory shape feature,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach Vessel pattern recognition using trajectory shape feature,

Reference 17

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T22:55:00.184849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation af9ff887-9cd7-4e82-8e92-934026bacde9 · outbound

This paper cites Deep Learning for Spatio-Temporal Data Mining: A Survey.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach Deep Learning for Spatio-Temporal Data Mining: A Survey

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:54:59.525732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 812cf0d5-9fde-4c74-b97f-b93d4e06699c · outbound

This paper cites Fréchet kernel for trajectory data analysis,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach Fréchet kernel for trajectory data analysis,

Reference 19

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T22:55:00.007226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5ae5cf28-568a-46d8-afa9-b38688f3e3ff · outbound

This paper cites Forward-backward selection with early dropping,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach Forward-backward selection with early dropping,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:01.498288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e3a00d0d-38bd-4e6a-8f93-5e4f7aa7778f · outbound

This paper cites A feature selection method for multi-dimension time-series data,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach A feature selection method for multi-dimension time-series data,

Reference 21

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T22:55:01.489094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 814a0a11-5374-468f-943f-07a887551930 · outbound

This paper cites An introduction to variable and feature selection,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach An introduction to variable and feature selection,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:01.480433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ffd03025-9813-40cd-b99e-03b922142ee5 · outbound

This paper cites Evolving feature selection: Syner- gistic backward and forward deletion method utilizing global feature importance,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach Evolving feature selection: Syner- gistic backward and forward deletion method utilizing global feature importance,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:01.470683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cf4d1c35-170c-48a1-b694-6376ee193edd · outbound

This paper cites Multi-dimensional feature selection and com- bination method of aerospace target based on k-means clustering and information entropy,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach Multi-dimensional feature selection and com- bination method of aerospace target based on k-means clustering and information entropy,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:01.459416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0dce6d55-4f4a-4261-9f31-d2cfd443155e · outbound

This paper cites Cost-Sensitive Feature Selection by Optimizing F-Measures.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach Cost-Sensitive Feature Selection by Optimizing F-Measures

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:54:59.825026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a709a3ce-02c6-4e74-b1ff-49c963a1e854 · outbound

This paper cites Data from: Movement tactics of a mobile predator in a meta-ecosystem with fluctuating resources: the arctic fox in the high arctic,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach Data from: Movement tactics of a mobile predator in a meta-ecosystem with fluctuating resources: the arctic fox in the high arctic,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:01.189521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 56fc00ac-67e5-4910-9d4c-9655ba864d39 · outbound

This paper cites Ais ship type codes reference,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach Ais ship type codes reference,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:00.914197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b8346813-e36c-40b2-acf0-6ae8d49ed273 · outbound

This paper cites Interna- tional best track archive for climate stewardship (ibtracs) project, version 4,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach Interna- tional best track archive for climate stewardship (ibtracs) project, version 4,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:00.642867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation af17ae5b-b809-486d-b154-c80afad1f24d · outbound

This paper cites Tabular data: Deep learning is not all you need,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach Tabular data: Deep learning is not all you need,

Reference 29

Resolution: verified exact; doi, observed 2026-08-06T22:54:59.396104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1d73f96b-7bc4-4cbc-aa21-d8434bff1f83 · outbound

This paper cites Random forests,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach Random forests,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T22:54:59.176786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62b76ef1-264e-4e73-be05-58db690dae70 · outbound

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

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach Xgboost: A scalable tree boosting system,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T22:54:59.257104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bcd46c8-a1dc-4713-8f32-dd9c9bcdc6fc · outbound

This paper cites Learning representations by back-propagating errors,.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach Learning representations by back-propagating errors,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:00.518287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

No inbound Pith citation observations are available.