Differential evolution for filter feature selection based on information theory and feature ranking

By Emrah Hançer, Bing Xue, Mengjie Zhang. Knowledge-Based Systems, 2018

357 citations recorded by the source corpus.

© 2017 Elsevier B.V. Feature selection is an essential step in various tasks, where filter feature selection algorithms are increasingly attractive due to their simplicity and fast speed. A common filter is to use mutual information to estimate the relationships between each feature and the class labels (mutual relevancy), and between each pair of features (mutual redundancy). This strategy has gained popularity resulting a variety of criteria based on mutual information. Other well-known strategies are to order each feature based on the nearest neighbor distance as in ReliefF, and based on the between-class variance and the within-class variance as in Fisher Score. However, each strategy comes with its own advantages and disadvantages. This paper proposes a new filter criterion inspired by the concepts of mutual information, ReliefF and Fisher Score. Instead of using mutual redundancy, the proposed criterion tries to choose the highest ranked features determined by ReliefF and Fisher Score while providing the mutual relevance between features and the class labels. Based on the proposed criterion, two new differential evolution (DE) based filter approaches are developed. While the former uses the proposed criterion as a single objective problem in a weighted manner, the latter considers the proposed criterion in a multi-objective design. Moreover, a well known mutual information feature selection approach (MIFS) based on maximum-relevance and minimum-redundancy is also adopted in single-objective and multi-objective DE algorithms for feature selection. The results show that the proposed criterion outperforms MIFS in both single objective and multi-objective DE frameworks. The results also indicate that considering feature selection as a multi-objective problem can generally provide better performance in terms of the feature subset size and the classification accuracy. © This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ee5ee93613bab3056b9d9a9581e17217c3b35300

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ayça Deniz
2. Tansel Dökeroğlu
3. Vijay Kumar
4. Hakan Ezgi Kiziloz
5. Vaishali
6. Dilbag Singh
7. Manjit Kaur

In Computational Theory and Mathematics

1. Ayça Deniz
2. Tansel Dökeroğlu
3. Vijay Kumar
4. Hakan Ezgi Kiziloz
5. Vaishali
6. Dilbag Singh
7. Manjit Kaur

In Computer Vision and Pattern Recognition

1. Ayça Deniz
2. Tansel Dökeroğlu
3. Vijay Kumar
4. Hakan Ezgi Kiziloz
5. Vaishali
6. Dilbag Singh
7. Manjit Kaur

Citations in the corpus

• A comprehensive survey on recent metaheuristics for feature selection (2022, methodology): “developed two new DE-based filter approaches for the feature selection problem [96].”
• Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks (2020, background): “DE algorithm has proven its potency in various domains [41, [43] [44] [45] .”