Hybrid learning based on Multiple Self-Organizing Maps and Genetic Algorithm
Date of Original Version
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. © 2011 IEEE.
Publication Title, e.g., Journal
Proceedings of the International Joint Conference on Neural Networks
Cai, Qiao, Haibo He, and Hong Man. "Hybrid learning based on Multiple Self-Organizing Maps and Genetic Algorithm." Proceedings of the International Joint Conference on Neural Networks (2011): 2313-2320. doi: 10.1109/IJCNN.2011.6033517.