Rough Set Based Ant-Lion Optimizer for Feature Selection

Title

Rough Set Based Ant-Lion Optimizer for Feature Selection

Creator

Azar A.T.; Banu N.; Koubaa A.

Description

As the area of computational intelligence evolves, the dimensionality of any sort of data gets expanded. To solve this issue, Rough Set Theory (RST) has been successfully used for finding reducts as it requires only supplied data and no additional information. This paper investigates a novel search strategy for minimal attribute reduction based on rough sets and Ant Lion Optimization (ALO). ALO is a nature-inspired algorithm that mimics the hunting mechanism of ant lions, and this is inspired to find the minimum reducts. Datasets from the UCI repository are used in this paper. The experimental results show that the features selected by the proposed method are well classified with reasonable accuracy. 2020 IEEE.

Source

Proceedings - 2020 6th Conference on Data Science and Machine Learning Applications, CDMA 2020, pp. 81-86.

Date

2020-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Ant-Lion Optimization; Feature Selection.; Rough set Theory

Coverage

Azar A.T., Robotics and Internet-of-Things Lab (RIOTU), Prince Sultan University, Riyadh, Saudi Arabia, Faculty of Computers and Artificial Intelligence, Benha University, Saudi Arabia; Banu N., Christ University, Bangalore Karnataka, India; Koubaa A., College of Computer and Information Sciences, Robotics and Internet-of-Things Lab (RIOTU), Prince Sultan University, Riyadh, Saudi Arabia

Rights

Restricted Access

Relation

ISBN: 978-172812746-0

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/CDMA47397.2020.00020

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083503823&doi=10.1109%2fCDMA47397.2020.00020&partnerID=40&md5=862e971c7f90f83a442016453b67696e