Journal Article

A Parallel Approach to Enhance the Performance of Supervised Machine Learning Realized in a Multicore Environment

Amsaad, Fathi, Ghimire, Ashutosh

2024

Overview

Machine learning models play a critical role in applications such as image recognition, natural language processing, and medical diagnosis, where accuracy and efficiency are paramount. As datasets grow in complexity, so too do the computational demands of classification techniques. Previous research has achieved high accuracy but required significant computational time. This paper proposes a parallel architecture for Ensemble Machine Learning Models, harnessing multicore CPUs to expedite performance. The primary objective is to enhance machine learning efficiency without compromising accuracy through parallel computing. This study focuses on benchmark ensemble models including Random Forest, XGBoost, ADABoost, and K Nearest Neighbors. These models are applied to tasks such as wine quality classification and fraud detection in credit card transactions. The results demonstrate that, compared to single-core processing, machine learning tasks run 1.7 times and 3.8 times faster for small and large datasets on quad-core CPUs, respectively.

• Publisher: MDPI AG
• Subject: Accuracy / Algorithms / Back propagation / Central processing units / Classification / Cognitive tasks / Computing time / CPUs / Data processing / Datasets / Efficiency / Electronic data processing / ensemble model / Image enhancement / Machine learning / Medical research / Methods / Microprocessors / multicore processing / Multiple core processors / Natural language processing / Neural networks / Optimization techniques / parallel computing / Regression analysis / Supervised learning / Task complexity / Traveling salesman problem