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hyperparameter optimization for Impulsive Sound Classifiers

Procedia Computer Science 2024年 237卷 618-625页

作者： André Mendes Paulo Trigo Joel Paulo ISEL – Instituto Superior de Engenharia de Lisboa Rua Conselheiro Emídio Navarro 1959-007 Lisbon Portugal

The detection and identification of impulsive sounds applied in a specific context, particularly in sports events, enables the analysis and synthesis of various metrics and statistics associated with the game or even the player's performance. In this context, the automatic identification of an impulsive sound, such as a ball being hit by a player, is a major contribution to the construction of a data source on which game-specific analysis can be performed. Considering all the characteristics (features) of a particular type of impulsive sound in various conditions/environments involves dealing with numerous variables, making it equally challenging to efficiently find the values of the hyperparameters that allow obtaining the best configuration for a given algorithm to be executed in a machine learning process. The contribution of this work is to explore the hyperparameter space in search of values that optimize the performance of the entire process of automatic impulsive sound classification. This process begins with the generation of the dataset to be processed, continues with the training of classification models, and ends with the evaluation of the learned models. The experiments consider a binary classification problem, where a distinction must be made between the intended event and noise. The validation of the process resorts to an audio extracted from videos of sports event competitions, specifically in tennis and padel, where the goal is to identify the sounds of racket hits on the ball. This work is currently in progress, but the preliminary results already enable to evidence the impact of hyperparameter optimization on the accuracy of the overall learning process.

关键词： Machine Learning Deep Learning Audio Analysis Sound Event Dataset hyperparameter optimization

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hyperparameter optimization with Genetic Algorithms and XGBoost: A Step Forward in Smart Grid Fraud Detection

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SENSORS 2024年第4期24卷 1230页

作者： Mehdary, Adil Chehri, Abdellah Jakimi, Abdeslam Saadane, Rachid Hassania Sch Publ Works LaGes Casablanca 20000 Morocco Royal Mil Coll Canada Dept Math & Comp Sci Kingston ON K7K 7B4 Canada Moulay Ismail Univ Fac Sci & Technol Errachidia GL ISI Team Meknes 50050 Morocco

This study provides a comprehensive analysis of the combination of Genetic Algorithms (GA) and XGBoost, a well-known machine-learning model. The primary emphasis lies in hyperparameter optimization for fraud detection in smart grid applications. The empirical findings demonstrate a noteworthy enhancement in the model's performance metrics following optimization, particularly emphasizing a substantial increase in accuracy from 0.82 to 0.978. The precision, recall, and AUROC metrics demonstrate a clear improvement, indicating the effectiveness of optimizing the XGBoost model for fraud detection. The findings from our study significantly contribute to the expanding field of smart grid fraud detection. These results emphasize the potential uses of advanced metaheuristic algorithms to optimize complex machine-learning models. This work showcases significant progress in enhancing the accuracy and efficiency of fraud detection systems in smart grids.

关键词： genetic algorithms XGBoost hyperparameter optimization fraud detection smart grids SGCC dataset electricity theft metaheuristic algorithms

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A Population-Based Hybrid Approach for hyperparameter optimization of Neural Networks

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IEEE ACCESS 2023年 11卷 50752-50768页

作者： Japa, Luis Serqueira, Marcello Mendonca, Israel Aritsugi, Masayoshi Bezerra, Eduardo Gonzalez, Pedro Henrique Kumamoto Univ Grad Sch Sci & Technol Kumamoto 8608555 Japan Fed Ctr Technol Educ Rio De Janeiro CEFET RJ BR-20271110 Rio De Janeiro Brazil Kumamoto Univ Fac Adv Sci & Technol Kumamoto 8608555 Japan Univ Fed Rio de Janeiro Syst Engn & Comp Sci Postgrad Program BR-21941914 Rio De Janeiro Brazil

hyperparameter optimization is a fundamental part of Auto Machine Learning (AutoML) and it has been widely researched in recent years;however, it still remains as one of the main challenges in this area. Motivated by the need of faster and more accurate hyperparameter optimization algorithms we developed HyperBRKGA, a new population-based approach for hyperparameter optimization. HyperBRKGA combines the Biased Random Key Genetic Algorithm with an Exploitation Method in order to search the hyperparameter space more efficiently than other commonly used hyperparameter optimization algorithms, such as Grid Search, Random Search, CMA-ES or Bayesian optimization. We develop and test two different alternatives for this Exploitation Method: Random Walk and Bayesian Walk. We also discuss and implement other schemes, such as a Training Data Reduction Strategy and a Diversity Control strategy, in order to further improve the efficacy of our method. We performed several computational experiments on 8 different datasets to assess the effectiveness of the proposed approach. Results showed that HyperBRKGA could find hyperparameter configurations that outperformed in terms of predictive quality the baseline methods in 6 out of 8 datasets while showing a reasonable execution time. Lastly, we conducted an ablation study and showed that the addition of every component was relevant to achieving high quality results.

关键词： Genetic algorithms hyperparameter optimization machine learning

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Seafood Quality Detection Using Electronic Nose and Machine Learning Algorithms With hyperparameter optimization

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IEEE ACCESS 2023年 11卷 62484-62495页

作者： Wijaya, Dedy Rahman Syarwan, Nailatul Fadhilah Nugraha, Muhammad Agus Ananda, Dahliar Fahrudin, Tora Handayani, Rini Telkom Univ Sch Appl Sci Bandung 40257 Indonesia Surabaya Telkom Inst Technol Dept Software Engn Surabaya 60231 Indonesia

The fisheries sector holds great importance in the Indonesian economy, particularly in terms of its contribution to growth and development. Both the fisheries and marine industries play significant roles in driving economic activities. The rising consumption of marine fishery products has resulted in a growing market demand for high-quality and safe products. Meeting this demand necessitates the maintenance of freshness in marine fishery products. Thus, this research aims to develop a fast, cheap, accurate method utilizing an electronic nose (e-nose) and machine learning algorithms as an alternative method for assessing the freshness quality of marine fishery products (seafood). This experiment employs seven algorithms with hyperparameter optimization to obtain the best performance. Machine learning algorithms are used for classification and regression tasks. The objective is to detect the freshness quality of marine fishery products accurately (classification task) while also identifying microbial populations present in the seafood (regression task). Through extensive investigations, the classification and regression models, specifically employing the k-Nearest Neighbors algorithm, demonstrated remarkable performance, achieving a very high accuracy score. Furthermore, the regression model yielded an RMSE value of 0.03 and an R-2 value of 0.995, indicating the effectiveness of the approach in assessing and quantifying the quality attributes of marine fishery products.

关键词： Seafood electronic nose machine learning algorithms hyperparameter optimization classification regression

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Toward Accurate Fused Deposition Modeling 3D Printer Fault Detection Using Improved YOLOv8 With hyperparameter optimization

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IEEE ACCESS 2023年 11卷 74251-74262页

作者： Karna, Nyoman Bogi Aditya Putra, Made Adi Paramartha Rachmawati, Syifa Maliah Abisado, Mideth Sampedro, Gabriel Avelino Telkom Univ Sch Elect Engn Bandung 40257 Indonesia STMIK Primakara Informat Engn Denpasar 80226 Indonesia Philippine Coding Camp Res & Dev Ctr Manila 0922 Philippines Natl Univ Coll Comp & Informat Technol Manila 1008 Philippines Open Univ Univ Philippines Fac Informat & Commun Studies Laguna 4301 Philippines De La Salle Univ Ctr Computat Imaging & Visual Innovat Manila 1004 Philippines

This research article presents an enhanced YOLOv8 model with an additional feature extraction layer integrated into the traditional YOLOv8 architecture to improve fault detection performance in smart additive manufacturing, specifically for FDM 3D printers. hyperparameter optimization techniques are employed to ensure the model is trained with optimal input and batch size configurations. The findings demonstrate that the additional module successfully enhances the model's performance in detecting faults during the FDM 3D printing process. The best results are achieved using the YOLOv8s model with an image input size of 640 and a batch size of 16, achieving a mAP(val)(50-95) of 89.7%. Despite the increased complexity from additional layers, there is a favorable trade-off between performance and complexity. Furthermore, a testbed implementation is conducted to validate the model's performance in a real-world setting, showing that the fault detection latency remains insignificant even with multiple Raspberry Pi clients. Overall, this research provides insights into improving fault detection in smart additive manufacturing and highlights the effectiveness of the proposed YOLOv8 model with additional extraction layers.

关键词： Additive manufacturing fault detection hyperparameter optimization testbed implementation YOLOv8

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Adaptive hyperparameter optimization for black-box adversarial attack

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INTERNATIONAL JOURNAL OF INFORMATION SECURITY 2023年第6期22卷 1765-1779页

作者： Guan, Zhenyu Zhang, Lixin Huang, Bohan Zhao, Bihe Bian, Song Beihang Univ Sch Cyber Sci & Technol Beijing Peoples R China

The study of adversarial attacks is crucial in the design of robust neural network models. In this work, we propose a hyperparameter optimization framework for black-box adversarial attacks. We observe that hyperparameters are extremely important to enhance the query efficiency of many black-box adversarial attack methods. Hence, we propose an adaptive hyperparameter tuning framework such that, in each query iteration, the attacker can adaptively selects the hyperparameter configuration based on the feedback from the victim to improve the attack success rate and query efficiency of the attack algorithm. The experiment results show, by adaptively tuning the attack hyperparameters, our technique outperforms the original algorithm, where the query efficiency is improved by 33.63% on the NES algorithm for untargeted attacks, 44.47% on the Bandits algorithm for untargeted attacks, and 32.24% improvement on the Bandits algorithm for targeted attacks.

关键词： Deep learning Adversarial attack Reinforcement learning hyperparameter optimization

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A survey on multi-objective hyperparameter optimization algorithms for machine learning

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第8期56卷 8043-8093页

作者： Morales-Hernandez, Alejandro Van Nieuwenhuyse, Inneke Gonzalez, Sebastian Rojas Hasselt Univ Fac Sci Hasselt Belgium Hasselt Univ VCCM Core Lab & Data Sci Inst Hasselt Belgium Univ Ghent Surrogate Modeling Lab Ghent Belgium

hyperparameter optimization (HPO) is a necessary step to ensure the best possible performance of Machine Learning (ML) algorithms. Several methods have been developed to perform HPO;most of these are focused on optimizing one performance measure (usually an error-based measure), and the literature on such single-objective HPO problems is vast. Recently, though, algorithms have appeared that focus on optimizing multiple conflicting objectives simultaneously. This article presents a systematic survey of the literature published between 2014 and 2020 on multi-objective HPO algorithms, distinguishing between metaheuristic-based algorithms, metamodel-based algorithms and approaches using a mixture of both. We also discuss the quality metrics used to compare multi-objective HPO procedures and present future research directions.

关键词： hyperparameter optimization Multi-objective optimization Metamodel Meta-heuristic Machine learning

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A novel hybrid PSO- and GS-based hyperparameter optimization algorithm for support vector regression

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NEURAL COMPUTING & APPLICATIONS 2023年第27期35卷 19961-19977页

作者： Acikkar, Mustafa Altunkol, Yunus Adana Alparslan Turkes Sci & Technol Univ Fac Comp & Informat Dept Software Engn TR-01250 Adana Turkiye Adana Alparslan Turkes Sci & Technol Univ Fac Engn Dept Elect & Elect Engn TR-01250 Adana Turkiye

hyperparameter optimization is vital in improving the prediction accuracy of support vector regression (SVR), as in all machine learning algorithms. This study introduces a new hybrid optimization algorithm, namely PSOGS, which consolidates two strong and widely used algorithms, particle swarm optimization (PSO) and grid search (GS). This hybrid algorithm was experimented on five benchmark datasets. The speed and the prediction accuracy of PSOGS-optimized SVR models (PSOGS-SVR) were compared to those of its constituent algorithms (PSO and GS) and another hybrid optimization algorithm (PSOGSA) that combines PSO and gravitational search algorithm (GSA). The prediction accuracies were evaluated and compared in terms of root mean square error and mean absolute percentage error. For the sake of reliability, the results of the experiments were obtained by performing 10-fold cross-validation on 30 runs. The results showed that PSOGS-SVR yields prediction accuracy comparable to GS-SVR, performs much faster than GS-SVR, and provides better results with less execution time than PSO-SVR. Besides, PSOGS-SVR presents more effective results than PSOGSA-SVR in terms of both prediction accuracy and execution time. As a result, this study proved that PSOGS is a fast, stable, efficient, and reliable algorithm for optimizing hyperparameters of SVR.

关键词： Support vector regression hyperparameter optimization Grid search Particle swarm optimization

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Genetic algorithm-based hyperparameter optimization of deep learning models for PM2.5 time-series prediction

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2023年第3期20卷 2959-2982页

作者： Erden, C. Sakarya Univ Appl Sci Fac Appl Sci Sakarya Turkiye Sakarya Univ Appl Sci AI Res & Applicat Ctr Sakarya Turkiye

Since air pollution negatively affects human health and causes serious diseases, accurate air pollution prediction is essential regarding environmental sustainability. Although conventional statistical and machine learning methods have been widely used for air quality forecasting, they have limitations in finding nonlinear relations and modeling sequential data. In recent years, deep learning methods such as long short-term memory, recurrent neural networks, and gated recurrent units have been successfully applied in several research areas, including time-series forecasting. In this study, deep learning algorithm is employed to predict the PM2.5 dataset, including air pollutants (NO, NO2, NOX, O-3, PM2.5, SO, and SO2) and meteorological features (wind speed, wind direction, and air temperature) in Istanbul metropolitan. Deep learning algorithms have many hyperparameters such as learning and dropout rate, the number of hidden layers and units in each hidden layer, activation function, loss function, and optimizer that need to be optimized in order to achieve optimal training performance. Therefore, a genetic algorithm-based hyperparameter optimization approach is proposed to find the best parameter combination. The prediction results of deep learning algorithms are compared with default hyperparameters and random search algorithms to confirm the efficacy of the genetic algorithm approach. The proposed method outperforms the other configurations, with the MSE error reduced by 13.38% and 55.30% for testing performance, respectively. The experimental results revealed that genetic algorithms are promising and applicable in hyperparameter optimization of deep neural network models, especially in air quality forecasting.

关键词： Gated recurrent units Genetic algorithms hyperparameter optimization Long-short term memory Particulate matter Recurrent neural networks

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Improved particle swarm optimization algorithm based on grouping and its application in hyperparameter optimization

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SOFT COMPUTING 2023年第13期27卷 8807-8819页

作者： Zhan, Jianjun Tang, Jun Pan, Qingtao Li, Hao Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China

In this article, an Improved Particle Swarm optimization (IPSO) is proposed for solving global optimization and hyperparameter optimization. This improvement is proposed to reduce the probability of particles falling into local optimum and alleviate premature convergence and the imbalance between the exploitation and exploration of the Particle Swarm optimization (PSO). The IPSO benefits from a new search policy named group-based update policy. The initial population of IPSO is grouped by the k-means to form a multisubpopulation, which increases the intragroup learning mechanism of particles and effectively enhances the balance between the exploitation and exploration. The performance of IPSO is evaluated on six representative test functions and one engineering problem. In all experiments, IPSO is compared with PSO and one other state-of-the-art metaheuristics. The results are also analyzed qualitatively and quantitatively. The experimental results show that IPSO is very competitive and often better than other algorithms in the experiments. The results of IPSO on the hyperparameter optimization problem demonstrate its efficiency and robustness.

关键词： Grouping policy Improved particle swarm optimization Multimodal function K-means hyperparameter optimization

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