Fusion Approaches of Feature Selection Algorithms for Classification Problems

Abstract

The large amount of data produced by applications in recent years needs to be analyzed in order to extract valuable underlying information from them. Machine learning algorithms are useful tools to perform this task, but usually it is necessary to reduce complexity of data using feature selection algorithms. As usual, many algorithms were proposed to reduce dimension of data, each one with its own advantages and drawbacks. The variety of algorithms leads to either choose one algorithm or to combine several methods. The last option usually brings better performance. Based on this, this paper proposes an analysis of two distinct approaches of combining feature selection algorithms (decision and data fusion). This analysis was made in supervised classification context using real and synthetic datasets. Results showed that one proposed approach (decision fusion) has achieved the best results for the majority of datasets.