Feature Grouping and Selection on High-Dimensional Microarray Data

Abstract

In classification tasks, as the dimensionality increases, the performance of the classifier improves until an optimal number of features is reached. Further increases of the dimensionality without increasing the number of training samples results in a degradation in classifier performance. This fact, called the curse of dimensionality, has become more relevant with the advent of larger datasets and the demands of Knowledge Discovery from Big Data. In this context, feature grouping has become an effective approach to provide additional information about relationships between features. In this work, we propose a greedy strategy, called GreedyPGG, that groups features based on the concept of Markov blankets. To such aim, we introduce the idea of predominant group of features. We also present an adaptation of the Variable Neighborhood Search (VNS) to high-dimensional feature selection that uses the GreedyPGG to reduce the search space. We test the effectiveness of the GreedyPGG on synthetic datasets and the VNS on microarray datasets. We compare VNS with popular and competitive strategies. Results show that GreedyPGG groups correlated features in an efficient way and that VNS is a competitive strategy, capable of finding a small number of features with high predictive power.