Compact NSGA-II for Multi-objective Feature Selection

arXiv:2402.12625v1 Announce Type: new
Abstract: Feature selection is an expensive challenging task in machine learning and data mining aimed at removing irrelevant and redundant features. This contributes to an improvement in classification accuracy, as well as the budget and memory requirements for classification, or any other post-processing task conducted after feature selection. In this regard, we define feature selection as a multi-objective binary optimization task with the objectives of maximizing classification accuracy and minimizing the number of selected features. In …

abstract accuracy arxiv budget classification cs.lg cs.ne data data mining feature features feature selection improvement machine machine learning memory mining multi-objective post-processing processing requirements type