A Hybrid Framework for Performance Optimization: Comparative Analysis of Machine Learning Algorithms in Data Mining

DOI

10.2991/978-94-6463-700-7_37How to use a DOI?

Keywords

Linear regression; Logistic regression; Decision tree; SVM; Naive Bayes; KNN; Random Forest algorithm

Abstract

Data mining has a significant function since it makes finding important information hidden in large data sets easy. However, the differences in problem sizes, data richness, heterogeneity, algorithmic bottlenecks, and constraints require new solutions for the highest efficiency. The present study aims to establish a composite approach that combines several data mining techniques to obtain higher accuracy, reliability, and performance. The work proceeds with the following empirical section that is squarely focused on providing an overview of common machine learning techniques and evaluating their advantage and drawback. Thus, the present hybrid framework enhanced certain characteristics of these algorithms in education, which solved the problem of overfitting, sensitivity of parameters, and data imbalance. In some cases, additional ensemble techniques and mathematical works are proposed to accomplish the methodology for varying datasets. The literature also shows that the proposed hybrid model learns from the results of the individual algorithms, excelling them in parameters such as accuracy and precision, particularly in execution time. The realistic application of the framework can be seen through some examples in contexts such as healthcare, finance, and energy consumption. This paper proposes a systematic way to hybridize, a valuable addition to the existing literature on machine learning and the development of a practical solution for current data mining problems. The conclusions reached point towards the opportunities in hybrid frameworks for reshaping the decision-making for the future development of the approach. In this research, we study the performance of different machine learning algorithms with reference to a dataset of patient attributes in diagnosing heart disease.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Sarita Naruka
AU  - Arvind Kumar Sharma
AU  - Amit Sharma
PY  - 2025
DA  - 2025/04/19
TI  - A Hybrid Framework for Performance Optimization: Comparative Analysis of Machine Learning Algorithms in Data Mining
BT  - Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)
PB  - Atlantis Press
SP  - 476
EP  - 483
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-700-7_37
DO  - 10.2991/978-94-6463-700-7_37
ID  - Naruka2025
ER  -