A Boosting Classification Approach Based on SOM

  • Hoa Dinh Nguyen PTIT
Keywords: Self organizing map, Adaptive boosting, Learning vector quantization

Abstract

Self-organizing map (SOM) is well known for its ability to visualize and reduce the dimension of the data. It has been a useful unsupervised tool for clustering problems for years. In this paper, a new classification framework based on SOM is introduced. In this approach, SOM is combined with the learning vector quantization (LVQ) to form a modified version of the SOM classifier, SOM-LVQ. The classification system is improved by applying an adaptive boosting algorithm with base learners are SOM-LVQ classifiers.  Two decision fusion strategies are adopted in Adaboost algorithm, which are majority voting and weighted voting. Experimental results based on a real dataset show that the newly proposed classification approach for SOM outperforms traditional supervised SOM. The results also suggest that this model can be applicable in real classification problems.

Published
2020-09-09