A practical three-stage hybrid feature selection method using discrete state transition algorithm

Dongning Song; Xiaojun Zhou

doi:10.1117/12.3004700

16 August 2023 A practical three-stage hybrid feature selection method using discrete state transition algorithm

Dongning Song, Xiaojun Zhou

Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 127870D (2023) https://doi.org/10.1117/12.3004700
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China

Abstract

The development of sensor technology has driven the growth of data scale and data dimensions. While the dimension of the dataset is rapidly increasing, efficiently processing high-dimensional and small-scale datasets is still a challenge for traditional machine learning methods. Currently, feature selection is an effective method used in this case. In this study, we propose a practical hybrid feature selection method called mRMR-FBSTA. The algorithm can be divided into three stages: the first stage is the filter part that provides a preliminary feature subset and necessary feature weights to avoid high computational costs caused by using only the wrapper part, the second stage called fast iteration stage which used to quickly find the acceptable solution and the third stage called global iteration stage that aims to find the global optimal solution based on the acceptable solution found by the fast iteration stage. The experimental results show that the proposed method achieves excellent performance on high-dimensional and small-scale datasets.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dongning Song and Xiaojun Zhou "A practical three-stage hybrid feature selection method using discrete state transition algorithm", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 127870D (16 August 2023); https://doi.org/10.1117/12.3004700

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available