Unsupervised Neural Networks based Scoring and Feature Selection in Biological Data Analysis

In this paper we present a novel method for scoring function specification and feature selection by combining unsupervised learning with supervised cross validation. A one dimensional Kohonen SOM is used to perform a clustering of object-data for a chosen subset of input features and given number of clusters. The resulting object clusters are compared with the predefined original object classes and a matching factor (score) is calculated. This score is used as criterion function for heuristic sequential feature selection.