Hybrid learning based on Multiple Self-Organizing Maps and Genetic Algorithm

Qiao Cai, Haibo He, Hong Man

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Multiple Self-Organizing Maps (MSOMs) based classification methods are able to combine the advantages of both unsupervised and supervised learning mechanisms. Specifically, unsupervised SOM can search for similar properties from input data space and generate data clusters within each class, while supervised SOM can be trained from the data via label matching in the global SOM lattice space. In this work, we propose a novel classification method that integrates MSOMs with Genetic Algorithm (GA) to avoid the influence of local minima. Davies-Bouldin Index (DBI) and Mean Square Error (MSE) are adopted as the objective functions for searching the optimal solution space. Experimental results demonstrate the effectiveness and robustness of our proposed approach based on several benchmark data sets from UCI Machine Learning Repository.

Original languageEnglish
Title of host publication2011 International Joint Conference on Neural Networks, IJCNN 2011 - Final Program
Pages2313-2320
Number of pages8
DOIs
StatePublished - 2011
Event2011 International Joint Conference on Neural Network, IJCNN 2011 - San Jose, CA, United States
Duration: 31 Jul 20115 Aug 2011

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2011 International Joint Conference on Neural Network, IJCNN 2011
Country/TerritoryUnited States
CitySan Jose, CA
Period31/07/115/08/11

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