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KGroups: A Versatile Univariate Max-Relevance Min-Redundancy Feature Selection Algorithm for High-dimensional Biological Data

Malick Ebiele, Malika Bendechache, Rob Brennan

Abstract

This paper proposes a new univariate filter feature selection (FFS) algorithm called KGroups. The majority of work in the literature focuses on investigating the relevance or redundancy estimations of feature selection (FS) methods. This has shown promising results and a real improvement of FFS methods' predictive performance. However, limited efforts have been made to investigate alternative FFS algorithms. This raises the following question: how much of the FFS methods' predictive performance depends on the selection algorithm rather than the relevance or the redundancy estimations? The majority of FFS methods fall into two categories: relevance maximisation (Max-Rel, also known as KBest) or simultaneous relevance maximisation and redundancy minimisation (mRMR). KBest is a univariate FFS algorithm that employs sorting (descending) for selection. mRMR is a multivariate FFS algorithm that employs an incremental search algorithm for selection. In this paper, we propose a new univariate mRMR called KGroups that employs clustering for selection. Extensive experiments on 14 high-dimensional biological benchmark datasets showed that KGroups achieves similar predictive performance compared to multivariate mRMR while being up to 821 times faster. KGroups is parameterisable, which leaves room for further predictive performance improvement through hyperparameter finetuning, unlike mRMR and KBest. KGroups outperforms KBest.

KGroups: A Versatile Univariate Max-Relevance Min-Redundancy Feature Selection Algorithm for High-dimensional Biological Data

Abstract

This paper proposes a new univariate filter feature selection (FFS) algorithm called KGroups. The majority of work in the literature focuses on investigating the relevance or redundancy estimations of feature selection (FS) methods. This has shown promising results and a real improvement of FFS methods' predictive performance. However, limited efforts have been made to investigate alternative FFS algorithms. This raises the following question: how much of the FFS methods' predictive performance depends on the selection algorithm rather than the relevance or the redundancy estimations? The majority of FFS methods fall into two categories: relevance maximisation (Max-Rel, also known as KBest) or simultaneous relevance maximisation and redundancy minimisation (mRMR). KBest is a univariate FFS algorithm that employs sorting (descending) for selection. mRMR is a multivariate FFS algorithm that employs an incremental search algorithm for selection. In this paper, we propose a new univariate mRMR called KGroups that employs clustering for selection. Extensive experiments on 14 high-dimensional biological benchmark datasets showed that KGroups achieves similar predictive performance compared to multivariate mRMR while being up to 821 times faster. KGroups is parameterisable, which leaves room for further predictive performance improvement through hyperparameter finetuning, unlike mRMR and KBest. KGroups outperforms KBest.

Paper Structure

This paper contains 22 sections, 16 equations, 6 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Feature Selection Methods
  4. Filter Feature Selection Methods
  5. Related Work
  6. General formulation of Max-Rel and mRMR
  7. Univariate FFS
  8. Multivariate FFS
  9. Challenges of the FS task
  10. Proposed Method
  11. Experimental Setup
  12. Classifiers
  13. Datasets
  14. Training Setup and Evaluation
  15. Experimental Results and Discussion
  16. ...and 7 more sections

Figures (6)

  • Figure 1: Overview of FFS methods on a tabular data as found in the literature.
  • Figure 2: Distribution of number of features selected by KBest, mRMR, and KGroups using Gini importance relevance estimator and KNeighbors, GaussianNB, RandomForest, XGB, MLP, and LinearSVC classification models.
  • Figure 3: Distribution of number of features selected by KBest, mRMR, and KGroups using mutual information relevance estimator and KNeighbors, GaussianNB, RandomForest, XGB, MLP, and LinearSVC classification models.
  • Figure 4: Distribution of number of features selected by KBest, mRMR, and KGroups using F-value relevance estimator and KNeighbors, GaussianNB, RandomForest, XGB, MLP, and LinearSVC classification models.
  • Figure 5: Boxplots of the execution time in seconds of KBest, mRMR, and KGroups.
  • ...and 1 more figures