Abstract
The curse of dimensionality problem occurs when the data are high-dimensional. It affects the learning process and reduces the accuracy. Feature selection is one of the dimensionality reduction approaches that mainly contribute to solving the curse of the dimensionality problem by selecting the relevant features. Irrelevant features are the dependent and redundant features that cause noise in the data and then reduce its quality. The main well-known feature-selection methods are wrapper and filter techniques. However, wrapper feature selection techniques are computationally expensive, whereas filter feature selection methods suffer from multicollinearity. In this research study, four new feature selection methods based on outlier detection using the Projection Pursuit method are proposed. Outlier detection involves identifying abnormal data (irrelevant features of the transpose matrix obtained from the original dataset matrix). The concept of outlier detection using projection pursuit has proved its efficiency in many applications but has not yet been used as a feature selection approach. To the author’s knowledge, this study is the first of its kind. Experimental results on nineteen real datasets using three classifiers (k-NN, SVM, and Random Forest) indicated that the suggested methods enhanced the classification accuracy rate by an average of 6.64% when compared to the classification accuracy without applying feature selection. It also outperformed the state-of-the-art methods on most of the used datasets with an improvement rate ranging between 0.76% and 30.64%. Statistical analysis showed that the results of the proposed methods are statistically significant.
Highlights
The present study focuses on developing feature selection (FS) based on outlier detection using PP called Projection Pursuit based Feature Selection (PPFS)
Fivethirtyeight/august-senate-polls, and Fifa2019 is from https://www.kaggle.com/karangadiya/fifa19
The PPFS methods have globally improved the classification accuracy compared to the accuracy obtained without applying FS
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The data is continuously evolving, leading to a big challenge in several domains. It is generally high-dimensional, which causes the curse of the dimensionality problem. We investigated this problem with a high number of attributes (features)
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