Experimental Study on Machine Learning and Metaheuristic Techniques in Dimensionality Reduction for Data Classification

Abstract

Development of new technology leads to tremendous growth of data. It is a great challenge to analyze and process such a vast quantity of data. The exploration of meaningful information from such huge data is really a challenging task, without any automated system. These data contain a large amount of features that may gain computational cost of the problem. The increasing dimensions of the features could also negatively impact on the classification process due to its superfluous, extraneous, and noisy features, increases the dimension of the problem. It ultimately leads to an increase in the computational time and decreases performance of the model. In such situations, dimensionality reduction plays a crucial role to reduce the dimension of such features. Dimensionality reduction removes the irrelevant, redundant, and noisy features without loosing much intended information. It simply reduces the number of features by considering only relevant features using feature reduction and feature selection techniques. Feature reduction techniques are used to transform the input feature into its equivalent new features by filtering out the most irrelevant, redundant, and noisy information. However, feature selection uses a search technique to select a subset of original features that can effectively describe the data. The selection of most relevant features from the high dimensional data is an NP-hard problem. It may increase the possible solution space exponentially with the increase in number of features. Therefore, it is essential to reduce the dimension of the features. It will ultimately increase the performance and decrease the computational cost of the problem. The objective of the thesis is two-fold: (i) firstly, design of new hybrid classification models by considering several feature reduction techniques such as principal component analysis and linear discriminant analysis along with various neuro-fuzzy models such as class belongingness neuro-fuzzy and linguistic neuro-fuzzy.