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An Efficient Classification Algorithm Based on T-Cells Maturation with No Parameters

Jungan Chen

College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, P. R. China

College of Electronic Information, Zhejiang Wanli University, Ningbo 315100, P. R. China

E-mail Address: friendcen21@hotmail.com

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Jinyin Chen

College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, P. R. China

E-mail Address: chenjinyin@zjut.edu.cn

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, and

Dongyong Yang

College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, P. R. China

E-mail Address: yangdy@zjut.edu.cn

Corresponding author.

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https://doi.org/10.1142/S1469026817500249Cited by:2 (Source: Crossref)

Abstract

In artificial immune system, many algorithms based on negative selection methods have been proposed to achieve satisfying classification performances. However, there are still many problems required to be solved, such as parameters sensibility and computational complexity. In this paper, a novel classification algorithm based on T-cells maturation algorithm was proposed for anomaly detection. Data set from UC Irvine Machine Learning Repository was used for 10-fold cross-validation, and simulation results confirmed its similar performances with AIRS. Compared with other classification algorithms based on negative selection methods, the proposed algorithm has no parameters and lower complexity, and can achieve satisfying classification results.

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