COMPARATIVE ANALYZING FEATURES SELECTION METHODS FOR DATA MINING TASKS

Mansurbek Qazaqov

Authors

Qazaqov Mansurbek Shomurot ugli Tashkent University of Information Technologies named after Muhammad al-Khwarizmi

Keywords:

wrapper method, fletcher method, features, machine learning

Abstract

This paper discusses feature selection methods for data mining and machine learning, focusing on three main approaches: wrapper, filter, and hybrid methods. These techniques help reduce dimensionality, improve computational efficiency, and enhance model accuracy by selecting the most relevant features and eliminating unnecessary data. Additionally, the paper presents a software tool designed to facilitate the feature selection process, utilizing the Java Data Mining API for efficient and scalable implementation. The software allows users to process large datasets and apply different feature selection techniques based on specific requirements. The paper also outlines the steps involved in the feature selection process, providing insights into its practical application. By combining a review of feature selection methods with a practical software solution, this study aims to assist researchers and practitioners in selecting the most suitable techniques for data preprocessing in machine learning. The findings contribute to improving model performance and optimizing computational resources, making machine learning applications more effective and efficient.

References

Jain A. K., Duin R. P. W., Mao J. Statistical pattern recognition: a review // IEEE Transactions on Pattern Analysis and Machine Intelligence. — 2000. — Vol. 22, № 1. — P. 4–37.

Yang Y., Pedersen J. O. A comparative study on feature selection // Proceedings of ACM International Conference on Research and Development in Information Retrieval. — 1999. — P. 42–49.

Mao J., Jain A. K. Artificial neural networks for feature selection and multivariate data projection // IEEE Transactions on Neural Networks. — 1995. — Vol. 6, № 2. — P. 296–317.

Witten I. H., Eibe F., Hall M. A. Data Mining: Practical Machine Learning Tools and Techniques. — 3rd ed. — Burlington: Morgan Kaufmann, 2011.

Li J., Cheng K., Wang S., Morstatter F., Trevino R. P., Tang J., Liu H. Feature Selection: A Data Perspective // Journal of Machine Learning Research. — 2017. — Vol. 18, № 1. — P. 1–67.

Sosa-Cabrera G., Gómez-Guerrero S., García-Torres M., Schaerer C. E. Feature Selection: A Perspective on Inter-Attribute Cooperation // Data Mining and Knowledge Discovery. — 2023. — Vol. 37, № 5.

Cao C., Zhang Q. A Contrast-Based Feature Selection Algorithm for High-Dimensional Datasets in Machine Learning // arXiv preprint. — 2024. — arXiv:2401.07482.

Wang H. A Novel Feature Selection Method Based on Quantum Support Vector Machine // arXiv preprint. — 2023. — arXiv:2311.17646.

Zhang Y., Wang X., Li Q. A Deep Learning-Based Feature Selection Method for Big Data // IEEE Access. — 2023. — Vol. 11. — P. 87654–87666.

Chen L., Zhao J., Sun Y. Feature Selection for High-Dimensional Data Using Reinforcement Learning // Expert Systems with Applications. — 2024. — Vol. 235. — P. 120563.