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feature selection

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/omelikechi/ipss

공식 코드

Integrated path stability selection

https://github.com/omelikechi/ipss_bioinformatics

공식 코드

Nonparametric IPSS: Fast, flexible feature selection with false discovery control

https://github.com/omelikechi/ipssr

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Nonparametric IPSS: Fast, flexible feature selection with false discovery control

https://github.com/oozdenizci/MMIDimReduction

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Stochastic Mutual Information Gradient Estimation for Dimensionality Reduction Networks

https://github.com/opdai/wsdm2022-xmrec-top1-solution

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An Effective Way for Cross-Market Recommendation with Hybrid Pre-Ranking and Ranking Models

https://github.com/osumray/harmonic_feature_selection

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Quiver Laplacians and Feature Selection

https://github.com/osumray/multiscale-signal-selection-single-cell

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Multiscale methods for signal selection in single-cell data

https://github.com/paper-submission-rafa/diversification-code

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Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees

https://github.com/parrt/stratx

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Nonparametric Feature Impact and Importance

https://github.com/patrickzib/SFA

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Multivariate Time Series Classification with WEASEL+MUSE

https://github.com/pennshenlab/freeform

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Knowledge-Driven Feature Selection and Engineering for Genotype Data with Large Language Models

https://github.com/phcavelar/coxae

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Multi-Omic Data Integration and Feature Selection for Survival-based Patient Stratification via Supervised Concrete Autoencoders

https://github.com/phython96/GNAS-MP

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Rethinking Graph Neural Architecture Search from Message-passing

https://github.com/pilancilab/LLM-Lasso

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LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization

https://github.com/pku-ml/non_neg

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Non-negative Contrastive Learning

https://github.com/plbenveniste/lungcancerrisk

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Development and external validation of a lung cancer risk estimation tool using gradient-boosting

https://github.com/pranoy-panda/Causal-Feature-Subset-Selection

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Instance-wise Causal Feature Selection for Model Interpretation

https://github.com/predict-idlab/powershap

공식 코드

Powershap: A Power-full Shapley Feature Selection Method

https://github.com/prgumd/SalientDSO

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SalientDSO: Bringing Attention to Direct Sparse Odometry

https://github.com/princengoc/s4s-final

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Improving random walk rankings with feature selection and imputation

https://github.com/pschydlo/ActionAnticipation

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Anticipation in Human-Robot Cooperation: A Recurrent Neural Network Approach for Multiple Action Sequences Prediction

https://github.com/psycoy/acl-2023-grouped-head-attention

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Finding the Pillars of Strength for Multi-Head Attention

https://github.com/pykao/ABCD-MICCAI2019

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Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet

https://github.com/qcpolimi/cqfs

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Feature Selection for Recommender Systems with Quantum Computing

https://github.com/qcpolimi/sigir22_quantumfeatureselection

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Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers

https://github.com/qianwangthu/feedback-nips2014-wq

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Attentional Neural Network: Feature Selection Using Cognitive Feedback

https://github.com/qisuqi/focuslearn

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FocusLearn: Fully-Interpretable, High-Performance Modular Neural Networks for Time Series

https://github.com/qrebjock/fanok

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FANOK: Knockoffs in Linear Time

https://github.com/r08in/gcrnn

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Sparse-Input Neural Network using Group Concave Regularization

https://github.com/rapidsai/cuml/tree/branch-23.10/cpp/include/cuml/genetic

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Accelerating Genetic Programming using GPUs

https://github.com/rdspring1/MISSION

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MISSION: Ultra Large-Scale Feature Selection using Count-Sketches

https://github.com/reczoo/RecZoo/tree/main/ranking/ctr/FinalMLP

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FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction

https://github.com/rehmanzafar/dlime_experiments

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DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems

https://github.com/rezacsedu/Convolutional-embedded-networks

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Convolutional Embedded Networks for Population Scale Clustering and Bio-ancestry Inferencing

https://github.com/rezacsedu/VariationDEC

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Convolutional Embedded Networks for Population Scale Clustering and Bio-ancestry Inferencing

https://github.com/rhshi/sparse-rf

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SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.