Review of feature selection for solving classification problems

Abstract

Classification of data crosses different domains has been extensively researched and is one of the basic methods for distinguishing one from another, as we need to know which belongs to which group. It has the capabilities to infer the unseen dataset with unknown class by analyzing its structural similarity to a given dataset with known classes. Reliability on classification results is very crucial issues. The higher the accuracy of generated classification results, the better the classifier is. There are constantly seeking to increase the accuracy of classification, either through existing techniques or through development of new ones. Different processes are applied to improve the accuracy of classification performance. While most existing methods addressed this task aim at improving the classifier techniques, this paper focused on reducing the number of features in dataset by selecting only the relevant features before giving the dataset to classifier. This motivates the need for sufficient methods that capable of selecting the relevant features with minimal information loss. The aim is to reduce the workload of classifier by using feature selection methods. With the focus on classification performance accuracy, this paper highlights and discusses the concept, abilities and application of feature selection for various applications in classification problem. From the review, classification with feature selection methods has shown impressive results with significant accuracy when compared to classification without feature selection.