Surrogate Interpretable Graph for Random Decision Forests | Paper Citation Record

Observation 2769d59f-2fbb-4f48-9080-b6d388037bda · outbound

This paper cites Random forests,.

Surrogate Interpretable Graph for Random Decision Forests Random forests,

Reference 1

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This paper cites Tree-based approaches for interpretable modeling in healthcare,.

Surrogate Interpretable Graph for Random Decision Forests Tree-based approaches for interpretable modeling in healthcare,

Reference 2

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Observation 9d76adde-92ad-4812-a372-5e68580a1996 · outbound

This paper cites A multicenter random forest model for effective prognosis prediction in collaborative clinical research network,.

Surrogate Interpretable Graph for Random Decision Forests A multicenter random forest model for effective prognosis prediction in collaborative clinical research network,

Reference 3

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Observation 853b0ea0-a064-4047-8dee-5e7e6889ec42 · outbound

This paper cites A random forest based biomarker discovery and power analysis framework for diagnostics research,.

Surrogate Interpretable Graph for Random Decision Forests A random forest based biomarker discovery and power analysis framework for diagnostics research,

Reference 4

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Observation 04e73e32-8ee0-49e9-90fd-f92626177493 · outbound

This paper cites Classification and interaction in random forests,.

Surrogate Interpretable Graph for Random Decision Forests Classification and interaction in random forests,

Reference 5

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Observation 3a3205cf-9c8a-4136-ade2-2b0fadd92445 · outbound

This paper cites Overview of random forest methodol- ogy and practical guidance with emphasis on computational biology and bioinformatics,.

Surrogate Interpretable Graph for Random Decision Forests Overview of random forest methodol- ogy and practical guidance with emphasis on computational biology and bioinformatics,

Reference 6

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This paper cites Feature learning for interpretable, performant decision trees,.

Surrogate Interpretable Graph for Random Decision Forests Feature learning for interpretable, performant decision trees,

Reference 7

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Observation 2c88acec-7f44-4f9a-9ba3-33c5120ed5ae · outbound

This paper cites Interaction forests: Identifying and exploiting interpretable interactions,.

Surrogate Interpretable Graph for Random Decision Forests Interaction forests: Identifying and exploiting interpretable interactions,

Reference 8

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Observation ca377be6-f73c-4cf1-b6cc-1a4eea39d88d · outbound

This paper cites Random forest for bioinformatics,.

Surrogate Interpretable Graph for Random Decision Forests Random forest for bioinformatics,

Reference 9

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Observation d7625b8c-88c0-4265-b17a-078a7b7234fc · outbound

This paper cites A random forest approach to capture genetic effects in the presence of population structure,.

Surrogate Interpretable Graph for Random Decision Forests A random forest approach to capture genetic effects in the presence of population structure,

Reference 10

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Observation 1a5c5ccc-f7ef-4eac-9fd9-8b9d9f43e6ba · outbound

This paper cites Perceptions and needs of artificial intelligence in health care to increase adoption: scoping review,.

Surrogate Interpretable Graph for Random Decision Forests Perceptions and needs of artificial intelligence in health care to increase adoption: scoping review,

Reference 11

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Observation 92aeafa4-7c8b-4584-a28c-f83d1825ad5d · outbound

This paper cites Explaining random forests using bipolar argumentation and markov networks,.

Surrogate Interpretable Graph for Random Decision Forests Explaining random forests using bipolar argumentation and markov networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:09.200049Z

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Observation 82491d16-9868-4889-be75-cc589f9214f1 · outbound

This paper cites Hierarchical shrinkage: Improving the accuracy and interpretability of tree-based models.,.

Surrogate Interpretable Graph for Random Decision Forests Hierarchical shrinkage: Improving the accuracy and interpretability of tree-based models.,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:09.186186Z

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Observation e64c6255-a13e-455a-ac26-881eb0822ace · outbound

This paper cites Learning interpretable rules for scalable data repre- sentation and classification,.

Surrogate Interpretable Graph for Random Decision Forests Learning interpretable rules for scalable data repre- sentation and classification,

Reference 14

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Observation 74990ac2-2518-45a8-9026-82b372f64480 · outbound

This paper cites Sirus: Stable and interpretable rule set for classification,.

Surrogate Interpretable Graph for Random Decision Forests Sirus: Stable and interpretable rule set for classification,

Reference 15

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Observation a0e32178-165e-4ba4-9b4b-69081a8506a0 · outbound

This paper cites Interpretable random forests via rule extraction,.

Surrogate Interpretable Graph for Random Decision Forests Interpretable random forests via rule extraction,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:09.145936Z

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Observation 5e6c35ba-906d-43e0-afbe-2083c120721c · outbound

This paper cites Multi-omics integration using random forests: Chal- lenges and opportunities in precision medicine,.

Surrogate Interpretable Graph for Random Decision Forests Multi-omics integration using random forests: Chal- lenges and opportunities in precision medicine,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:09.132137Z

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Observation 1138a813-60bf-4bc9-9194-f0cfe925ae39 · outbound

This paper cites Interaction forests: Technical report,.

Surrogate Interpretable Graph for Random Decision Forests Interaction forests: Technical report,

Reference 18

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Observation 88dcbd2a-2450-4e99-93ce-953e782c4e9a · outbound

This paper cites Consistent Individualized Feature Attribution for Tree Ensembles.

Surrogate Interpretable Graph for Random Decision Forests Consistent Individualized Feature Attribution for Tree Ensembles

Reference 19

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Observation c6037e0d-6cbe-4f52-be7d-5009625cd6a0 · outbound

This paper cites From local explanations to global understanding with explainable ai for trees,.

Surrogate Interpretable Graph for Random Decision Forests From local explanations to global understanding with explainable ai for trees,

Reference 20

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Observation 968cf487-a15a-4761-b4a8-d9e94d8a5fd0 · outbound

This paper cites Beyond treeshap: Efficient computation of any-order shapley interactions for tree ensembles,.

Surrogate Interpretable Graph for Random Decision Forests Beyond treeshap: Efficient computation of any-order shapley interactions for tree ensembles,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:09.094960Z

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Observation 9cc3fc9d-b8c2-4255-bf22-0519566a126b · outbound

This paper cites AI Readiness in Healthcare through Storytelling XAI.

Surrogate Interpretable Graph for Random Decision Forests AI Readiness in Healthcare through Storytelling XAI

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:50:08.618433Z

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Observation e70113c0-0c38-4076-a545-a05556f58ac3 · outbound

This paper cites A nested model for ai design and validation,.

Surrogate Interpretable Graph for Random Decision Forests A nested model for ai design and validation,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:09.079634Z

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Observation 344e7e8f-7cf2-4c71-93c1-813e812b1252 · outbound

This paper cites A random forest-based approach to identifying the most informative seasonality tests,.

Surrogate Interpretable Graph for Random Decision Forests A random forest-based approach to identifying the most informative seasonality tests,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:09.066107Z

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Observation 43d8f595-0086-4af9-b13c-284bc03717b5 · outbound

This paper cites Linear tree shap,.

Surrogate Interpretable Graph for Random Decision Forests Linear tree shap,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:09.051532Z

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Observation c81d55ed-04d3-4ff7-815f-bce17a2e9b74 · outbound

This paper cites Trained random forests completely reveal your dataset,.

Surrogate Interpretable Graph for Random Decision Forests Trained random forests completely reveal your dataset,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:09.037944Z

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Observation a5cb61e5-a0a4-438a-ad5c-11ffb07ad0cf · outbound

This paper cites Iterative random forests to discover predictive and stable high-order interactions,.

Surrogate Interpretable Graph for Random Decision Forests Iterative random forests to discover predictive and stable high-order interactions,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:09.024159Z

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Observation 370fda5b-0a3d-4f4b-b812-163b6197e60c · outbound

This paper cites Generalized random forests,.

Surrogate Interpretable Graph for Random Decision Forests Generalized random forests,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:09.010479Z

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Observation d9f5f990-ca5b-47d2-aa87-de6655bc94df · outbound

This paper cites Deep exploration of random forest model boosts the interpretability of machine learning studies of complicated immune responses and lung burden of nanoparticles,.

Surrogate Interpretable Graph for Random Decision Forests Deep exploration of random forest model boosts the interpretability of machine learning studies of complicated immune responses and lung burden of nanoparticles,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:08.997061Z

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Observation 13a07842-220f-4993-9b70-2d248fd184cd · outbound

This paper cites A unified approach to interpreting model predictions,.

Surrogate Interpretable Graph for Random Decision Forests A unified approach to interpreting model predictions,

Reference 30

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Observation 04f300be-9133-457c-a2e7-d8e95e72030e · outbound

This paper cites A new method for graph-based representation of text in natural language processing,.

Surrogate Interpretable Graph for Random Decision Forests A new method for graph-based representation of text in natural language processing,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:08.974873Z

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Observation 6c34523d-6bbc-4ef7-bb8d-2cb85861ac7a · outbound

This paper cites Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping,.

Surrogate Interpretable Graph for Random Decision Forests Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:08.961856Z

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Observation b7de27c8-8868-4be0-b115-a57eca148aac · outbound

This paper cites Interaction forests: Identifying and exploiting interpretable quantitative and qualitative interaction effects,.

Surrogate Interpretable Graph for Random Decision Forests Interaction forests: Identifying and exploiting interpretable quantitative and qualitative interaction effects,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:08.948771Z

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Observation be3428ea-97c9-4fce-b8b6-0439563df0e6 · outbound

This paper cites Interpreting random forest analysis of ecological models to move from prediction to explanation,.

Surrogate Interpretable Graph for Random Decision Forests Interpreting random forest analysis of ecological models to move from prediction to explanation,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:08.935572Z

Source-reported events for the cited work

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Observation 90d9718a-68bc-44dd-af2c-1ecfca008de1 · outbound

This paper cites Graph random forest: a graph embedded algorithm for identifying highly connected important features,.

Surrogate Interpretable Graph for Random Decision Forests Graph random forest: a graph embedded algorithm for identifying highly connected important features,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:08.922779Z

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Observation 31921c1e-85d0-4e46-bfd2-ff2a6a62b47f · outbound

This paper cites Improving the explainability of random forest classifier–user centered approach,.

Surrogate Interpretable Graph for Random Decision Forests Improving the explainability of random forest classifier–user centered approach,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:08.909896Z

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Observation 32fb7730-5459-419b-b77d-c369e73b995b · outbound

This paper cites Random forest model and sample explainer for non-experts in machine learning–two case studies,.

Surrogate Interpretable Graph for Random Decision Forests Random forest model and sample explainer for non-experts in machine learning–two case studies,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:08.896814Z

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Observation 7bccc686-f93b-41b7-b2e3-005358e2ee29 · outbound

This paper cites Geometry-and accuracy-preserving random forest proximities,.

Surrogate Interpretable Graph for Random Decision Forests Geometry-and accuracy-preserving random forest proximities,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:08.883879Z

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Observation e3e7dfa0-6780-48ea-b4d4-2321e3379e2a · outbound

This paper cites Interpreting tree ensembles with intrees,.

Surrogate Interpretable Graph for Random Decision Forests Interpreting tree ensembles with intrees,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:08.870760Z

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Observation bea26cb8-32ad-45ca-b4b8-1c15802fa5d3 · outbound

This paper cites A comparison among interpretative proposals for random forests,.

Surrogate Interpretable Graph for Random Decision Forests A comparison among interpretative proposals for random forests,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:08.858108Z

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Observation 0bffc50a-258a-45e8-9823-0efaa8529410 · outbound

This paper cites Connecting interpretability and robustness in decision trees through separation,.

Surrogate Interpretable Graph for Random Decision Forests Connecting interpretability and robustness in decision trees through separation,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:08.845693Z

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Observation 87419ec8-5ba2-4b14-8a13-b166ecb3d28b · outbound

This paper cites A framework for inherently interpretable optimization models,.

Surrogate Interpretable Graph for Random Decision Forests A framework for inherently interpretable optimization models,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:08.832237Z

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Observation 5a18af0d-a8bb-489b-893d-e082c2206f35 · outbound

This paper cites Optimal decision trees for categorical data via integer programming,.

Surrogate Interpretable Graph for Random Decision Forests Optimal decision trees for categorical data via integer programming,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:08.819150Z

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Observation 42c4d038-26c3-4e68-aa6a-d4c5879a651c · outbound

This paper cites Optimal interpretable decision trees using integer linear programming techniques,.

Surrogate Interpretable Graph for Random Decision Forests Optimal interpretable decision trees using integer linear programming techniques,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:08.806203Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 23a18ffd-3511-41cb-9f72-0698d4ba349e · outbound

This paper cites Forest-ore: Mining an optimal rule ensemble to interpret random forest models,.

Surrogate Interpretable Graph for Random Decision Forests Forest-ore: Mining an optimal rule ensemble to interpret random forest models,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:08.793513Z

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Observation b3f90e38-664f-49b4-ba62-0fdc7d2cb157 · outbound

This paper cites On Representing Linear Programs by Graph Neural Networks.

Surrogate Interpretable Graph for Random Decision Forests On Representing Linear Programs by Graph Neural Networks

Reference 46

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Source-reported events for the cited work

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Observation 8257043d-dce5-4d80-872c-7ba5e6993b16 · outbound

This paper cites Deep graph matching meets mixed-integer linear programming: Relax at your own risk ?.

Surrogate Interpretable Graph for Random Decision Forests Deep graph matching meets mixed-integer linear programming: Relax at your own risk ?

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:50:08.585915Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2fc16e96-fd70-40c0-a91c-98ebfa66df76 · outbound

This paper cites A mixed integer linear programming method for optimizing layout of irrigated pumping well in oasis,.

Surrogate Interpretable Graph for Random Decision Forests A mixed integer linear programming method for optimizing layout of irrigated pumping well in oasis,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:08.780716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c58a3b52-b1d9-438f-a8e2-f215d444498b · outbound

This paper cites Towards Foundation Models for Mixed Integer Linear Programming.

Surrogate Interpretable Graph for Random Decision Forests Towards Foundation Models for Mixed Integer Linear Programming

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T20:50:08.189115Z

Source-reported events for the cited work

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Observation bc7c0e87-1043-4347-acd3-67d62cdeb543 · outbound

This paper cites Liu, A Scalable Graph-based Mixed-Integer Linear Programming Approach for the Examina- tion Timetabling Problem.

Surrogate Interpretable Graph for Random Decision Forests Liu, A Scalable Graph-based Mixed-Integer Linear Programming Approach for the Examina- tion Timetabling Problem

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:08.766682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1feed8f3-0ce9-4ffb-a57c-36811eab2e25 · outbound

This paper cites Chronic kidney disease dataset.

Surrogate Interpretable Graph for Random Decision Forests Chronic kidney disease dataset

Reference 51

Resolution: verified exact; raw_fallback, observed 2026-08-15T20:50:08.554506Z

Source-reported events for the cited work

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Observation 05161c79-5066-4938-aefb-90afd4e810ce · outbound

This paper cites Air quality and health impact dataset.

Surrogate Interpretable Graph for Random Decision Forests Air quality and health impact dataset

Reference 52

Resolution: verified exact; raw_fallback, observed 2026-08-15T20:50:08.487601Z

Source-reported events for the cited work

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Observation cae4bcca-c668-4394-9a33-87ff43771f6d · outbound

This paper cites A deep instance generative framework for milp solvers under limited data availability,.

Surrogate Interpretable Graph for Random Decision Forests A deep instance generative framework for milp solvers under limited data availability,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:08.753402Z

Source-reported events for the cited work

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Observation 26b73ec2-e321-4e7b-af00-5733152f8b4c · outbound

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

Surrogate Interpretable Graph for Random Decision Forests Xgboost: A scalable tree boosting system,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:08.740539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 15eb82ce-5168-458d-b118-8f597c0ea224 · outbound

This paper cites A general framework for identifying hierarchical interactions and its application to genomics data,.

Surrogate Interpretable Graph for Random Decision Forests A general framework for identifying hierarchical interactions and its application to genomics data,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:08.727113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 99856b13-330b-43bb-a163-6dc9dcecf6ca · outbound

This paper cites Heart Disease.

Surrogate Interpretable Graph for Random Decision Forests Heart Disease

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-15T20:50:08.221016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28f2fa4a-d90e-4bf1-97a1-dbd09746f9fb · outbound

This paper cites Using the adap learning algorithm to forecast the onset of diabetes mellitus,.

Surrogate Interpretable Graph for Random Decision Forests Using the adap learning algorithm to forecast the onset of diabetes mellitus,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:08.713711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0d534cec-b68b-4598-bf7d-9517481678bc · outbound

This paper cites Alzheimer’s disease dataset.

Surrogate Interpretable Graph for Random Decision Forests Alzheimer’s disease dataset

Reference 58

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T20:50:08.411631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cd7821fa-bdb9-4933-a8de-f592e515dd88 · outbound

This paper cites an unresolved cited work.

Surrogate Interpretable Graph for Random Decision Forests Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:50:08.699333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 72ea38a7-961c-4d23-bcdb-7068ae90a302 · outbound

This paper cites an unresolved cited work.

Surrogate Interpretable Graph for Random Decision Forests Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:50:08.686273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a906f844-8626-4980-8a86-ebde1482c030 · outbound

This paper cites an unresolved cited work.

Surrogate Interpretable Graph for Random Decision Forests Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:50:08.673130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 09c5b5c6-9b89-4c6e-a610-35511d1d6b4a · outbound

This paper cites an unresolved cited work.

Surrogate Interpretable Graph for Random Decision Forests Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:50:08.659202Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a9690819-7900-4c8c-aa50-0e5b068470da · outbound

This paper cites an unresolved cited work.

Surrogate Interpretable Graph for Random Decision Forests Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:50:08.646097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers