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
Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor Networks
Artyom Nikitin, Andrei Chertkov, Rafael Ballester-Ripoll, Ivan Oseledets, Evgeny Frolov
Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers
Maurizio Ferrari Dacrema, Fabio Moroni, Riccardo Nembrini, Nicola Ferro, Guglielmo Faggioli, Paolo Cremonesi
Deep Feature Screening: Feature Selection for Ultra High-Dimensional Data via Deep Neural Networks
Kexuan Li, Fangfang Wang, Lingli Yang, Ruiqi Liu
Empirical Analysis of Lifelog Data using Optimal Feature Selection based Unsupervised Logistic Regression (OFS-ULR) Model with Spark Streaming
Sadhana Tiwari, Sonali Agarwal