文献类型：会议论文

英文题名：IPSO: An Immune based PSO Supervised Learning System for Incremental Learning

作者：Zhou, Xuan[1];Yu, Jin[1];Qi, Rongbin[1];Qian, Feng[1];Wang, Zhenlei[1]

机构：[1]E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

会议论文集：8th World Congress on Intelligent Control and Automation (WCICA)

会议日期：JUL 06-09, 2010

会议地点：Jinan, PEOPLES R CHINA

语种：英文

外文关键词：PSO; artificial immune system; classification; incremental learning

摘要：PSO has been proved as an effective supervised learning system in recent years, but it's not an effective method for incremental learning problems. Aiming at the incremental learning target for classification, a hybrid algorithm of Particle Swarm Optimization (PSO) and Artificial Immune System (AIS) called Immune based PSO (IPSO) is presented in this paper. IPSO inherits the incremental learning ability of AIS. In IPSO, training data is presented to the algorithm one by one, and the training proceed is a one-shot incremental algorithm. Besides, the swarm does not converge to a single solution; instead, each particle is a part of the classifier, and the whole memory population is taken as the integral classifier to the problem. Compared the results of standard PSO and IPSO in several benchmark problems from the UCI data sets, we found that IPSO achieved a better classification accuracy than standard PSO in most cases. It is also competitive with some of the algorithms most commonly used for classification.