HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization

TL;DR

HyP-ABC introduces a hybrid evolutionary algorithm for hyper-parameter tuning that improves efficiency and reduces parameter tuning complexity, demonstrated on multiple ML models with real-world data.

Contribution

The paper presents HyP-ABC, a novel hybrid optimization algorithm based on modified artificial bee colony, specifically designed for efficient hyper-parameter tuning in machine learning.

Findings

HyP-ABC outperforms existing methods in efficiency.

It requires fewer parameters to tune.

It achieves robust performance on real-world data.

Abstract

Machine learning techniques lend themselves as promising decision-making and analytic tools in a wide range of applications. Different ML algorithms have various hyper-parameters. In order to tailor an ML model towards a specific application, a large number of hyper-parameters should be tuned. Tuning the hyper-parameters directly affects the performance (accuracy and run-time). However, for large-scale search spaces, efficiently exploring the ample number of combinations of hyper-parameters is computationally challenging. Existing automated hyper-parameter tuning techniques suffer from high time complexity. In this paper, we propose HyP-ABC, an automatic innovative hybrid hyper-parameter optimization algorithm using the modified artificial bee colony approach, to measure the classification accuracy of three ML algorithms, namely random forest, extreme gradient boosting, and support vector machine. Compared to the state-of-the-art techniques, HyP-ABC is more efficient and has a limited number of parameters to be tuned, making it worthwhile for real-world hyper-parameter optimization problems. We further compare our proposed HyP-ABC algorithm with state-of-the-art techniques. In order to ensure the robustness of the proposed method, the algorithm takes a wide range of feasible hyper-parameter values, and is tested using a real-world educational dataset.