Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics

TL;DR

This paper explores how nature-inspired algorithms, particularly evolutionary algorithms, can be applied to feature selection and dimension reduction to improve data analytics efficiency in large-scale, high-dimensional datasets.

Contribution

It introduces the application of nature-inspired algorithms for dimension reduction, addressing the curse of dimensionality in data science, with practical examples across various domains.

Findings

Effective reduction of data dimensionality using evolutionary algorithms.

Improved computational efficiency in large-scale data analysis.

Successful application in image processing, sentiment analysis, and network traffic analysis.

Abstract

In [1], we have explored the theoretical aspects of feature selection and evolutionary algorithms. In this chapter, we focus on optimization algorithms for enhancing data analytic process, i.e., we propose to explore applications of nature-inspired algorithms in data science. Feature selection optimization is a hybrid approach leveraging feature selection techniques and evolutionary algorithms process to optimize the selected features. Prior works solve this problem iteratively to converge to an optimal feature subset. Feature selection optimization is a non-specific domain approach. Data scientists mainly attempt to find an advanced way to analyze data n with high computational efficiency and low time complexity, leading to efficient data analytics. Thus, by increasing generated/measured/sensed data from various sources, analysis, manipulation and illustration of data grow exponentially. Due to the large scale data sets, Curse of dimensionality (CoD) is one of the NP-hard problems in data science. Hence, several efforts have been focused on leveraging evolutionary algorithms (EAs) to address the complex issues in large scale data analytics problems. Dimension reduction, together with EAs, lends itself to solve CoD and solve complex problems, in terms of time complexity, efficiently. In this chapter, we first provide a brief overview of previous studies that focused on solving CoD using feature extraction optimization process. We then discuss practical examples of research studies are successfully tackled some application domains, such as image processing, sentiment analysis, network traffics / anomalies analysis, credit score analysis and other benchmark functions/data sets analysis.