Optimal Dimensionality Reduction

Abstract

In mining large, high-dimensional sparse featured datasets, it is important to reduce the dimensionality for efficient processing. Some methods of reducing the features include conventional feature selection and extraction methods, frequent item support-based methods, and optimal feature selection approaches. In earlier chapters, we discussed feature selection based on frequent items. In the present chapter, we combine a nonlossy compression scheme with genetic algorithm-based feature selection in arriving at a scheme that results in efficient feature selection. In the process, we provide an overview of methods of feature selection, feature extraction, genetic algorithms, etc. We implement the proposed scheme of efficient optimal prototype selection using genetic algorithms that combines compressed data classification performance as a fitness function. We demonstrate working of the scheme by implementing it on a large dataset bringing out insights, and sensitivity of genetic operators is shown as a movement of cloud of solution space as the parameters vary. We provide notes on relevant literature and a list of references at the end of the chapter.