An improved Michigan particle swarm optimization for classification

Author

Wu, Pei ; Liu, Ruochen ; Ma, Jingjing ; Li, Yangyang

Author_Institution

Key Lab. of Intell. Perception & Image Understanding of Minist. of Educ. of China, Xidian Univ., Xi´´an, China

fYear

2011

fDate

11-15 April 2011

Firstpage

1

Lastpage

7

Abstract

Classification is one of the most frequently occurring tasks of human decision making. In this paper, two improved versions of Michigan particle swarm optimization (MPSO), Improved MPSO1 (IMPSO1) and Improved MPSO2 (IMPSO2), are proposed. IMPSO1 adopts a adaptive inertia factor so as to flexibly control the search path, moreover, both nearest neighbor (NN) and 5-NN classification are used so as to take more local information into account and improve the diversity of population. In IMPSO2, a new selection operator is introduced into MPSO to obtain a competitive classification success rate as well as a lower computation cost. The proposed algorithm has been extensively compared with PSO, MPSO, C4.5, 1-NN and 3-NN over eight UCI data sets. The result of experiment indicates the superiority of the algorithm over other four algorithms on classification success rate.

Keywords

decision making; particle swarm optimisation; pattern classification; 5-NN classification; IMPSO1; IMPSO2; MPSO; classification success rate; human decision making; improved Michigan particle swarm optimization; nearest neighbor classification; search path; selection operator; Algorithm design and analysis; Classification algorithms; Diabetes; Iris; Particle swarm optimization; Prototypes; Training; Classification; Particle Swarm; Selection Operator; Swarm Intelligence;

fLanguage

English

Publisher

ieee

Conference_Titel

Swarm Intelligence (SIS), 2011 IEEE Symposium on

Conference_Location

Paris

Print_ISBN

978-1-61284-053-6

Type

conf

DOI

10.1109/SIS.2011.5952570

Filename

5952570