Deriving meaningful rules from gene expression data for classification

We propose a novel scheme for designing fuzzy rule based classifiers for gene expression data analysis. A neural network based method is used for selecting a set of informative genes. Considering only these selected set of genes, we cluster the expression data with a fuzzy clustering algorithm. Each cluster is then converted into a fuzzy if-then rule, which models an area in the input space. These rules are tuned using a gradient descent technique to improve the classification performance. The rule base is tested on a leukemia data set containing two classes and it is found to produce excellent results. The membership functions associated with the rules are then analyzed and the rule base is further simplified without compromising the classification accuracy. The most attractive attributes of the proposed scheme are: it is an automatic extraction scheme; unlike other classifiers, it produces human interpretable rules, and it is not expected to give bad generalization as fuzzy rules do not respond to areas not represented by the training data.