LIOR ROKACH

Feature set partitioning generalizes the task of feature selection by partitioning the feature set into subsets of features that are collectively useful, rather than by finding a single useful subset of features. This paper presents a novel feature set partitioning approach that is based on a genetic algorithm. As part of this new approach a new encoding schema is also proposed and its properties are discussed. We examine the effectiveness of using a Vapnik-Chervonenkis dimension bound for evaluating the fitness function of multiple, oblivious tree classifiers. The new algorithm was tested on various datasets and the results indicate the superiority of the proposed algorithm to other methods.