Comprehensive Approach to Feature Selection

This publication has 74 references indexed in Scilit:

• A taxonomy of unsupervised feature selection methods including their pros, cons, and challenges
  The Journal of Supercomputing, 2024
• A many objective based feature selection model for software defect prediction
  Concurrency and Computation: Practice and Experience, 2024
• A comparative analysis of meta-heuristic optimization algorithms for feature selection on ML-based classification of heart-related diseases
  The Journal of Supercomputing, 2023
• A novel binary many-objective feature selection algorithm for multi-label data classification
  International Journal of Machine Learning and Cybernetics, 2021
• A dividing-based many-objective evolutionary algorithm for large-scale feature selection
  Soft Computing, 2019
• A review of unsupervised feature selection methods
  Artificial Intelligence Review, 2019
• Fast-mRMR: Fast Minimum Redundancy Maximum Relevance Algorithm for High-Dimensional Big Data
  International Journal of Intelligent Systems, 2016
• Nature inspired feature selection meta-heuristics
  Artificial Intelligence Review, 2015
• A comprehensive empirical comparison of modern supervised classification and feature selection methods for text categorization
  Journal of the Association for Information Science and Technology, 2014
• Multi-objective Feature Selection with NSGA II
  2007