Simple Deterministic Selection-Based Genetic Algorithm for Hyperparameter Tuning of Machine Learning Models

Raji, Ismail Damilola; Bello-Salau, Habeeb; Umoh, Ime J.; Onumanyi, A. J.; Adegboye, Mutiu Adesina; Salawudeen, Ahmed Tijani

doi:10.3390/app12031186

Cited by 32 publications

(15 citation statements)

References 39 publications

(41 reference statements)

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“…This process is repeated until either a satisfactory solution is found or a specified number of iterations have passed. Genetic algorithms are commonly used in machine learning and data analysis to find optimal model parameters [33][34][35] or identify patterns in data 36,37 . The same approach is applied to feature selection in the proposed work.…”

Section: B Evolutionary Algorithmsmentioning

confidence: 99%

Bird's Eye View Feature Selection for High-Dimensional Data

Belhaouari

Shakeel

Erbad

et al. 2023

Preprint

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In machine learning, an informative dataset is crucial for accurate predictions. However, high dimensional data often contains irrelevant features, outliers, and noise, which can negatively impact model performance and consume computational resources. To tackle this challenge, the Bird's Eye View (BEV) feature selection technique is introduced. This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. The proposed strategy in this paper leads to improved classification performance and a reduced number of features compared to conventional methods, as demonstrated by outperforming state-of-the-art feature selection techniques across multiple benchmark datasets.

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Section: B Evolutionary Algorithmsmentioning

confidence: 99%

Bird's Eye View Feature Selection for High-Dimensional Data

Belhaouari

Shakeel

Erbad

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Although the random search method is suitable for large datasets, its results are quite different ( 21 ). Genetic algorithm and particle swarm optimization are swarm optimization algorithms, but they require sufficient initial points and the optimal solution can only be efficiently discovered through low-efficiency training ( 22 , 23 ).…”

Section: Problem Descriptionmentioning

confidence: 99%

Bayesian Global Optimization Gated Recurrent Unit Model for Human-Driven Vehicle Lane-Change Trajectory Prediction Considering Hyperparameter Optimization

Chen

Zhao

et al. 2023

Transportation Research Record: Journal of the Transportation R

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To address the low accuracy and inefficiency of current lane-change trajectory prediction methods for human-driven vehicles, this study develops a neural network lane-change trajectory prediction model with hyperparametric optimization capability using Bayesian optimization and gated recurrent units to consider the effect of lane-change intention on vehicle lane-change behavior and to predict it. The proposed model was instantiated using trajectory data of 8,721 vehicles. The results show that the overall recognition accuracy of intention recognition under the optimal input is 93.54%, and the recognition accuracy of keeping straight, left lane-change and right lane-change is 95.59%, 91.72%, and 93.30%, respectively. The root mean square errors of the predicted and actual trajectories to the left and to the right under the optimal input are 0.2582 and 0.2957, respectively. This paper demonstrates that, for the intention recognition module, the low-dimensional input enables the model to obtain high prediction accuracy, while for the trajectory prediction module, the high-latitude input enables the model to obtain a low prediction error. The developed trajectory prediction model can be used to assist in driving decision-making, path planning, and so forth.

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“…Current research has focused on hyperparameter optimization, not just using ML classifiers alone, as evidenced by the literature study [42]. Other studies focus on the tuning and optimization of hyperparameter values in order to improve the performance of models [43][44][45][46].…”

Section: Related Workmentioning

confidence: 99%

Medical Internet-of-Things Based Breast Cancer Diagnosis Using Hyperparameter-Optimized Neural Networks

et al. 2022

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In today’s healthcare setting, the accurate and timely diagnosis of breast cancer is critical for recovery and treatment in the early stages. In recent years, the Internet of Things (IoT) has experienced a transformation that allows the analysis of real-time and historical data using artificial intelligence (AI) and machine learning (ML) approaches. Medical IoT combines medical devices and AI applications with healthcare infrastructure to support medical diagnostics. The current state-of-the-art approach fails to diagnose breast cancer in its initial period, resulting in the death of most women. As a result, medical professionals and researchers are faced with a tremendous problem in early breast cancer detection. We propose a medical IoT-based diagnostic system that competently identifies malignant and benign people in an IoT environment to resolve the difficulty of identifying early-stage breast cancer. The artificial neural network (ANN) and convolutional neural network (CNN) with hyperparameter optimization are used for malignant vs. benign classification, while the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) were utilized as baseline classifiers for comparison. Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models. We employ a particle swarm optimization (PSO) feature selection approach to select more satisfactory features from the breast cancer dataset to enhance the classification performance using MLP and SVM, while grid-based search was used to find the best combination of the hyperparameters of the CNN and ANN models. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used to test the proposed approach. The proposed model got a classification accuracy of 98.5% using CNN, and 99.2% using ANN.

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Simple Deterministic Selection-Based Genetic Algorithm for Hyperparameter Tuning of Machine Learning Models

Cited by 32 publications

References 39 publications

Bird's Eye View Feature Selection for High-Dimensional Data

Bird's Eye View Feature Selection for High-Dimensional Data

Bayesian Global Optimization Gated Recurrent Unit Model for Human-Driven Vehicle Lane-Change Trajectory Prediction Considering Hyperparameter Optimization

Medical Internet-of-Things Based Breast Cancer Diagnosis Using Hyperparameter-Optimized Neural Networks

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