A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

Document Type

Article

Publication Date

2-1-2024

Abstract

A dandelion algorithm (DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA, which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained; while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection (CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

Identifier

85184363905 (Scopus)

Publication Title

IEEE/CAA Journal of Automatica Sinica

External Full Text Location

https://doi.org/10.1109/JAS.2023.124008

e-ISSN

23299274

ISSN

23299266

First Page

377

Last Page

390

Issue

2

Volume

11

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