TY - GEN
T1 - Hybrid learning based on Multiple Self-Organizing Maps and Genetic Algorithm
AU - Cai, Qiao
AU - He, Haibo
AU - Man, Hong
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=80054731428&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80054731428&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2011.6033517
DO - 10.1109/IJCNN.2011.6033517
M3 - Conference contribution
AN - SCOPUS:80054731428
SN - 9781457710865
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 2313
EP - 2320
BT - 2011 International Joint Conference on Neural Networks, IJCNN 2011 - Final Program
T2 - 2011 International Joint Conference on Neural Network, IJCNN 2011
Y2 - 31 July 2011 through 5 August 2011
ER -