A Distributed Feature Selection Approach Based on a Complexity Measure

Feature selection is often required as a preliminary step for many machine learning problems. However, most of the existing methods only work in a centralized fashion, i.e. using the whole dataset at once. In this paper we propose a new methodology for distributing the feature selection process by samples which maintains the class distribution. Subsequently, it performs a merging procedure which updates the final feature subset according to the theoretical complexity of these features, by using data complexity measures. In this way, we provide a framework for distributed feature selection independent of the classifier and that can be used with any feature selection algorithm. The effectiveness of our proposal is tested on six representative datasets. The experimental results show that the execution time is considerably shortened whereas the performance is maintained compared to a previous distributed approach and the standard algorithms applied to the non-partitioned datasets.