An Efficient Wrapper-KNN Based Feature Selection Method for High Dimensional Data Using Machine Learning on a Ray Framework

Subhash Kamble,; Arunalatha J S,; Venugopal K R

Author(s)

Subhash Kamble, Department of Computer Science and Engineering University Visvesvaraya College of Engineering,India

Arunalatha J S, Department of Computer Science and Engineering University Visvesvaraya College of Engineering, india

Venugopal K R Department of Computer Science and Engineering University Visvesvaraya College of Engineering,india

Abstract

A variety of disciplines have handled large databases that contain an enormous number of features. While several attempts have been made to design an optimized model for Feature Selection (FS) in high dimensional data systems, the difficulty of processing such data remains a great challenge. As a result, the high dimensionality of large datasets can hinder the Machine Learning (ML) process. Feature selection is aimed at reducing features that are unnecessary, noisy or redundant that may affect classification. To address this issue Parallelized Exhaustive Wrapper-Based Feature Selection Technique using a ray distributed framework is proposed. In this, K-Nearest Neighbour (KNN) classifier algorithm and Cross Validation (CV) approach is utilized to deal with overfitting problems to obtain better results. The experimental outcomes show that the proposed approach retains the exceptional efficiency of the model while reducing computation time considerably. The overall average accuracy of the proposed work is 92.24%, and the execution time taken is better than as compared with the state-of-art techniques.

Publication Details

Page(s) 411-419

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 5, Year: 2024

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Keywords

machine learning, cross validation, Big Data, Feature selection, KNearest Neighbour

References

. .This work presents a new paradigm based on KNN and a wrapper-based approach. The proposed work employs a K-Nearest Neighbour Classifier to train the model with an integration of parallelized exhaustive feature selection in a multicore CPU environment. The possibility and reliability of the experimental analysis of eight datasets were carried out. The proposed model succeeded in obtaining a significant reduction in size of the feature subset, and exhibits the effectiveness in terms of classification accuracy and computation time of the model. The model obtains an average accuracy measured up to 92.24% for all the datasets. References, : .

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