Feature Selection for Classification using Principal Component Analysis and Information Gain

Feature Selection and classification have previously been widely applied in various areas like business, medical and media fields. High dimensionality in datasets is one of the main challenges that has been experienced in classifying data, data mining and sentiment analysis. Irrelevant and redundant attributes have also had a negative impact on the complexity and operation of algorithms for classifying data. Consequently, the algorithms record poor results or performance. Some existing work use all attributes for classification, some of which are insignificant for the task, thereby leading to poor performance. This paper therefore develops a hybrid filter model for feature selection based on principal component analysis and information gain. The hybrid model is then applied to support classification using machine learning techniques e.g. the Naive Bayes technique. Experimental results demonstrate that the hybrid filter model reduces data dimensions, selects appropriate feature sets, and reduces training time, hence providing better classification performance as measured by accuracy, precision and recall..

Automated summary

This study investigates the application of feature selection and classification in various fields, addressing the challenges of high dimensionality in datasets and the negative impact of irrelevant and redundant attributes on classification algorithms. To improve classification performance, a hybrid filter model based on principal component analysis and information gain is proposed and applied to machine learning techniques, demonstrating enhanced accuracy, precision, and recall.