# Multi-objective Binary Coordinate Search for Feature Selection

arXiv: https://arxiv.org/abs/2402.12616 · HTML: https://mlsanity.com/paper/2402.12616/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 33 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Builds on (most cited first, 2 of 2)

- [Coordinate Descent Algorithms](https://mlsanity.com/paper/1502.04759/) (2015)
- [Evolutionary Computation in Action: Feature Selection for Deep Embedding Spaces of Gigapixel Pathology Images](https://mlsanity.com/paper/2303.00943/) (2023)

## Built on by (most cited first, 1 of 1)

- [Enhancing Diversity in Multi-objective Feature Selection](https://mlsanity.com/paper/2407.17795/) (2024)

## Abstract (arXiv)

A supervised feature selection method selects an appropriate but concise set of features to differentiate classes, which is highly expensive for large-scale datasets. Therefore, feature selection should aim at both minimizing the number of selected features and maximizing the accuracy of classification, or any other task. However, this crucial task is computationally highly demanding on many real-world datasets and requires a very efficient algorithm to reach a set of optimal features with a limited number of fitness evaluations. For this purpose, we have proposed the binary multi-objective coordinate search (MOCS) algorithm to solve large-scale feature selection problems. To the best of our knowledge, the proposed algorithm in this paper is the first multi-objective coordinate search algorithm. In this method, we generate new individuals by flipping a variable of the candidate solutions on the Pareto front. This enables us to investigate the effectiveness of each feature in the corresponding subset. In fact, this strategy can play the role of crossover and mutation operators to generate distinct subsets of features. The reported results indicate the significant superiority of our method over NSGA-II, on five real-world large-scale datasets, particularly when the computing budget is limited. Moreover, this simple hyper-parameter-free algorithm can solve feature selection much faster and more efficiently than NSGA-II.