Dimensionality Reduction: An Empirical Study on the Usability of IFE-CF (Independent Feature Elimination- by C-Correlation and F-Correlation) Measures

TL;DR

This paper empirically evaluates the effectiveness of IFE-CF measures for dimensionality reduction, focusing on removing redundant features to improve classification accuracy using LVQ on medical datasets.

Contribution

It introduces an empirical assessment of IFE-CF measures for feature selection, demonstrating their utility in enhancing classification performance on benchmark datasets.

Findings

Redundant features negatively impact classification accuracy.

IFE-CF measures effectively identify and remove redundant attributes.

Improved accuracy observed with reduced feature sets.

Abstract

The recent increase in dimensionality of data has thrown a great challenge to the existing dimensionality reduction methods in terms of their effectiveness. Dimensionality reduction has emerged as one of the significant preprocessing steps in machine learning applications and has been effective in removing inappropriate data, increasing learning accuracy, and improving comprehensibility. Feature redundancy exercises great influence on the performance of classification process. Towards the better classification performance, this paper addresses the usefulness of truncating the highly correlated and redundant attributes. Here, an effort has been made to verify the utility of dimensionality reduction by applying LVQ (Learning Vector Quantization) method on two Benchmark datasets of 'Pima Indian Diabetic patients' and 'Lung cancer patients'.