Chapter 14 - Review on hybrid feature selection and classification of microarray gene expression data

Abstract

Microarray gene expression data are widely used in identifying the classes in cancer data for diagnosis. Cancer is caused by abnormal cell growth in an organ and spreads into other organs of the human body. Classification of microarray gene expression data based on selected features is one of the predominant healthcare applications in biomedical research. Relevant features are selected from the dataset by searching a subset of features and evaluating the subset to select the optimal one. In this chapter, different techniques involved in feature selection, hybridization of feature selection techniques, and data classification based on reduced features are reviewed and the performance of several techniques is analyzed using different metrics. Feature selection can be applied in datasets that are labeled or not labeled. It is used in identifying the feature subset that is optimal from the given dataset. Such reduced feature dataset does not have any negative impact on the classification accuracy. A metaheuristic search algorithm is used in feature selection. These can be categorized as population-based and neighborhood search techniques. The searched subsets of features are evaluated with a classification algorithm to select the best subset of features. To select the population-based, global optimal features, evolutionary search algorithms such as genetic algorithm (GA) and differential evolution (DE) and swarm intelligence algorithms such as ant colony optimization (ACO) and particle swarm optimization (PSO) are employed. The neighborhood-based tabu search algorithm is used to find the neighborhood's best features. Classifiers like nearest neighbor, support vector machine, fuzzy rough nearest neighbor, etc., are used to evaluate the subsets of features and select the optimal subset of features. The results of several feature selection algorithms are studied, and eventually, the hybrid feature selection algorithm produces an enhanced performance based on the results of various performance metrics.