Convergence of Big Data Technologies and Computational Intelligent Techniques

0565535 - ÚI 2024 RIV US eng M - Monography Chapter
Kalina, Jan
Robust Dimensionality Reduction: A Resistant Search for the Relevant Information in Complex Data.
Convergence of Big Data Technologies and Computational Intelligent Techniques. Hershey: IGI Global, 2023 - (Gupta, G.), s. 186-210. ISBN 9781668452646
R&D Projects: GA ČR(CZ) GA22-02067S
Institutional support: RVO:67985807
Keywords : Computational Intelligence * Complexity Reduction * Machine Learning * Variable Selection * Outlying Measurements * Robustness * Sparsity * Comprehensibility
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://dx.doi.org/10.4018/978-1-6684-5092-5.ch004

With the increasing availability of massive data in various fields of applications such as engineering, economics, or biomedicine, there appears an urgent need for new reliable tools for obtaining relevant knowledge from such data, which allow one to find and interpret the most relevant features (variables). Such interpretation is however infeasible for the habitually used methods of machine learning, which can be characterized as black boxes. This chapter is devoted to variable selection methods for finding the most relevant variables for the given task. After explaining general principles, attention is paid to robust approaches, which are suitable for data contaminated by outlying values (outliers). Three main approaches to variable selection (prior, intrinsic, and posterior) are explained, and their recently proposed examples are illustrated on applications related to credit risk management and molecular genetics. These examples reveal recent robust approaches to data analysis to be able to outperform non-robust tools.
Permanent Link: https://hdl.handle.net/11104/0337062