Feature Selection
Feature selection aims to identify the most relevant subset of features from a larger dataset, improving model performance, interpretability, and efficiency. Current research emphasizes developing novel algorithms, including those based on neural networks (e.g., RelChaNet), genetic algorithms, and large language models (LLMs), to select features effectively and efficiently, often incorporating techniques like causal inference and uncertainty quantification. These advancements are crucial for various applications, such as medical diagnosis, financial prediction, and recommender systems, where reducing dimensionality and improving model explainability are paramount. The field is also actively exploring new evaluation metrics and addressing challenges like fairness and privacy in feature selection.
Papers
REFRESH: Responsible and Efficient Feature Reselection Guided by SHAP Values
Shubham Sharma, Sanghamitra Dutta, Emanuele Albini, Freddy Lecue, Daniele Magazzeni, Manuela Veloso
FSDR: A Novel Deep Learning-based Feature Selection Algorithm for Pseudo Time-Series Data using Discrete Relaxation
Mohammad Rahman, Manzur Murshed, Shyh Wei Teng, Manoranjan Paul
Comparative Analysis of Data Preprocessing Methods, Feature Selection Techniques and Machine Learning Models for Improved Classification and Regression Performance on Imbalanced Genetic Data
Arshmeet Kaur, Morteza Sarmadi
A Bio-Medical Snake Optimizer System Driven by Logarithmic Surviving Global Search for Optimizing Feature Selection and its application for Disorder Recognition
Ruba Abu Khurma, Esraa Alhenawi, Malik Braik, Fatma A. Hashim, Amit Chhabra, Pedro A. Castillo