Diversity between neural networks and decision trees for building multiple classifier systems

A multiple classifier system can only improve the performance when the members in the system are diverse from each other. Combining some methodologically different techniques is considered a constructive way to expand the diversity. This paper investigates the diversity between the two different data mining techniques, neural networks and automatically induced decision trees. Input decimation through salient feature selection is also explored in the paper in the hope of acquiring further diversity. Among various diversities defined, the coincident failure diversity (CFD) appears to be an effective measure of useful diversity among classifiers in a multiple classifier system when the majority voting decision strategy is applied. A real-world medical classification problem is presented as an application of the techniques. The constructed multiple classifier systems are evaluated with a number of statistical measures in terms of reliability and generalisation. The results indicate that combined MCSs of the nets and trees trained with the selected features have higher diversity and produce better classification results.