Gene Expressions Classification using Semisupervised Adaptive Subspace Self-organizing Map

S.-I. Wu and B. Colvin (USA)


Neural Networks, Data Mining, Time Frequency Analysis, Computing in Science


We have implemented several neural networks in solving classification problems. We have tried the Multi-Level Perceptron (MLP), Self-Organization Map (SOM), and Adaptive Subspace Self-Organization Map (ASSOM) systems. We have applied these systems in classification of gene expression and artificially generated data sets. In general, ASSOM performs better than MLP and SOM in the sense that ASSOM is more accurate and efficient.

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