Enhancing Machine Learning Through Advanced Optimized Parallelized Model Aggregation: a Novel Theory of Optimized Parallelized Ensemble Learning (OPEL)

EasyChair Preprint 15362

Abstract

This study presents a parallelized multi-mode ensemble learning framework to optimize computational efficiency, speed and model accuracy, which is a novel framework for optimizing machine learning ensemble multi-model selection using parallelized, execution and voting mechanisms, using a proposed theory, ‘Optimized Parallelized Ensemble Learning’ (OPEL), for optimized voting. By formulating theoretical mathematical models to guide model selection, weighting, and parallel execution strategies and utilizing performance metrics like the Matthews correlation coefficient to select top-performing models, with parallel processing incorporated to enhance efficiency, experimental simulations were conducted on real-world datasets using high-performance computing platform. Coupled with comparative analysis with traditional methods, reveals improved computation speed and accuracy under varying conditions. This paper henceforth introduced key innovations, which include the Parallelized Model Execution (PME) approach, Consensus-Based Model Selection (CMS), and Optimized Parallel Voting Mechanism (OPVM), each contributing to reduced computational time and improved model performance. The study demonstrates significant gains in computational speed and accuracy through parallelization and advanced voting techniques, with a time complexity reduction as defined by Amdahl's Law. The proposed ensemble learning framework is validated as both computationally efficient and robust in diverse, large-scale AI applications.

Keyphrases: Accuracy, Optimization, Parallelization, efficiency, semble, voting

@booklet{EasyChair:15362,
  author    = {Jephter Pelekamoyo and Libati Hastings M. and Derrick Ntalasha},
  title     = {Enhancing Machine Learning Through Advanced Optimized Parallelized Model Aggregation: a Novel Theory of Optimized Parallelized Ensemble Learning (OPEL)},
  howpublished = {EasyChair Preprint 15362},
  year      = {EasyChair, 2024}}