Evolutionary Algorithms and Efficient Data Analytics for Image Processing

Mohammadi, Farid Ghareh; Shenavarmasouleh, Farzan; Amini, M. Hadi; Arabnia, Hamid R.

doi:10.48550/arxiv.1907.12914

Cited by 1 publication

(1 citation statement)

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“…Mohammadi et al provided a comprehensive review of evolutionary-based image processing on real-time processing in universal image steganalysis and detected hidden messages in images. Since training ML models is time-consuming, the authors argued that the use of algorithms such as the ABC algorithm is of great benefit to solve NP-hard problems caused by CoD [31]. In [32], the authors proposed two algorithms to modify two steps of the original ABC.…”

Section: B Abc Applicationsmentioning

confidence: 99%

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

Zahedi¹,

Mohammadi²,

Amini³

2021

Preprint

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Machine learning techniques lend themselves as promising decision-making and analytic tools in a wide range of applications. Different ML algorithms have various hyperparameters. 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 hyperparameters 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.

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Section: B Abc Applicationsmentioning

confidence: 99%