Comparative Review of Feature Selection and Classification modeling

Feature selection is a procedure in machine learning to find a subset of features that help to know the most important features of the data set for model construction. It removes irrelevant and redundant information as much as possible. Fewer attributes is desirable because it reduces the complexity of the model. In many classification problems, it is difficult to learn good classifiers before removing these unwanted features due to the huge size of the data. Reducing the number of irrelevant or redundant features can drastically reduce the running time of the learning algorithms and yields a more general classifier. This helps in getting a better insight into the concept of a classification problem. The objective of feature selection is improving prediction performance and providing a better understanding of the process data. In this paper, we provide an overview of some of the classification methods. Most of these classification methods are based on some threshold values and benchmark algorithms that determine the optimality of the features in the dataset. Selecting the optimal set of features will help us hence reduce the space and time complexity as well as increase the accuracy or purity of classification. We also review some of the feature selection techniques on standard datasets to illustrate the applicability of feature selection techniques.