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A comparative analysis of ensemble learning algorithms with hyperparameter optimization for soil liquefaction prediction

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ENVIRONMENTAL EARTH SCIENCES 2024年第9期83卷 289-289页

作者： Demir, Alparslan Serhat Kurnaz, Talas Fikret Koekcam, Abdullah Hulusi Erden, Caner Dagdeviren, Ugur Sakarya Univ Fac Engn Dept Ind Engn Sakarya Turkiye Mersin Univ Tech Sci Vocat Sch Transportat Serv Mersin Turkiye Sakarya Univ Appl Sci AI Res & Applicat Ctr Sakarya Turkiye Kutahya Dumlupinar Univ Fac Engn Dept Civil Engn Kutahya Turkiye Sakarya Univ Appl Sci Fac Technol Dept Comp Engn Sakarya Turkiye

Accurate prediction of soil liquefaction potential is crucial for evaluating the stability of structures in earthquake regions. This study focuses on predicting soil liquefaction using a dataset that included historical liquefaction cases from the 1999 Turkey and Taiwan earthquakes. The dataset was divided into three subsets: Dataset A (fine-grained), Dataset B (coarse-grained), and Dataset C (all samples). Through the analysis of these subsets, the study aims to assess the performance of machine learning algorithms in predicting soil liquefaction potential. This study applied ensemble machine learning algorithms, including extreme gradient boosting, adaptive boosting, extra trees, bagging classifiers, light gradient boosting machine, and random forest, to accurately classify the liquefaction potential of fine-grained and coarse-grained soils. A comparison between the genetic algorithm approach for hyperparameter optimization and traditional methods such as grid search and random search revealed that genetic algorithms outperformed both in terms of average test and train accuracy. Specifically, the light gradient boosting machine yielded the best predictions of soil liquefaction potential among the algorithms tested. The study demonstrated that Dataset B achieved the highest learning performance with accuracy of 0.92 on both the test and training sets. Furthermore, Dataset A showed a training accuracy of 0.88 and a test accuracy of 0.84, while Dataset C exhibited a training accuracy of 0.87 and a test accuracy of 0.87. Future studies could build on these findings by evaluating the performance of genetic algorithms on a wider range of machine learning algorithms and datasets, thus advancing our understanding of soil liquefaction prediction and its implications for geotechnical engineering.

关键词： Soil liquefaction Soil types hyperparameter optimization Ensemble learning Machine learning

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Deep learning hyperparameter optimization: Application to electricity and heat demand prediction for buildings

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ENERGY AND BUILDINGS 2023年第1期289卷

作者： Morteza, Azita Yahyaeian, Amir Abbas Mirzaeibonehkhater, Marzieh Sadeghi, Sina Mohaimeni, Ali Taheri, Saman Stevens Inst Technol Dept Civil Environm & Ocean Engn 1 Castle Point Terrace Hoboken NJ 07030 USA Indiana Univ Purdue Univ Purdue Sch Engn & Technol Dept Mech Engn Indianapolis IN 46202 USA Indiana Univ Purdue Univ Purdue Sch Engn & Technol Dept Mech Engn Indianapolis IN 46202 USA North Carolina State Univ Dept Chem & Biomol Engn Raleigh NC 27695 USA Isfahan Univ Technol Dept Chem Engn Esfahan *** Iran

Optimal planning and operation studies of modern energy systems are tied up with medium to long-term predictions of energy demand. Deep learning algorithms have recently become significantly useful in this regard because of their capacity to learn load patterns with a high degree of fluctuation and uncertainty. Yet, empirical evaluation of different configurations of deep learning models, for enhancing prediction accuracy, has been mostly ignored in the preceding literature. In this paper, different architectures of deep recurrent neural networks (DRNNs) are explored and adjusted specifically for the purpose of performing medium- and long-term predictions of energy demands. We intend to create a bespoke DRNN for heating and electricity consumption prediction with a 1-h resolution. Moreover, hyperparameter optimization, which is a time-consuming and rigorous task in deep learning algorithms due to their abundance, dependence on the particular application, and empirical nature, is studied comprehensively. In this respect, the space of possible configuration variables is thoroughly explored to identify a collection of hyperparameters that enables the DRNN model to more accurately forecast energy consumption. We also benchmark the proposed algorithm's performance against other data-driven models, namely support vector machine (SVM) and gradient boosting (GB) regression. The proposed model outperforms SVM by 5.4% and GB regression by 7.0% in terms of energy forecasting accuracy.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Energy consumption Machine learning Deep neural network hyperparameter optimization

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Towards Efficient Multiobjective hyperparameter optimization: A Multiobjective Multi-fidelity Bayesian optimization and Hyperband Algorithm 17th

Towards Efficient Multiobjective Hyperparameter Optimization...

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17th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Chen, Zefeng Zhou, Yuren Huang, Zhengxin Xia, Xiaoyun Sun Yat Sen Univ Sch Artificial Intelligence Zhuhai 519082 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Youjiang Med Univ Nationalities Dept Comp Sci Baise 533000 Peoples R China Jiaxing Univ Coll Informat Sci & Engn Jiaxing 314001 Peoples R China

ISBN: (纸本)9783031147142;9783031147135

Developing an efficient solver for hyperparameter optimization (HPO) can help to support the environmental sustainability of modern AI. One popular solver for HPO problems is called BOHB, which attempts to combine the benefits of Bayesian optimization (BO) and Hyperband. It conducts the sampling of configurations with the aid of a BO surrogate model. However, only the few high-fidelity measurements are utilized in the building of BO surrogate model, leading to the fact that the built BO surrogate cannot well model the objective function in HPO. Especially, in the scenario of multiobjective optimization (which is more complicated than single-objective optimization), the resultant BO surrogates for modelling all conflicting objective functions would be more likely to mislead the configuration search. To tackle this low-efficiency issue, in this paper, we propose an efficient algorithm, referred as Multiobjective Multi-Fidelity Bayesian optimization and Hyperband, for solving multiobjective HPO problems. The key idea is to fully consider the contributions of computationally cheap low-fidelity surrogates and expensive high-fidelity surrogates, and enable effective utilization of the integrated information of multi-fidelity ensemble model in an online manner. The weight-ages for distinct fidelities are adaptively determined based on the approximation performance of their corresponding surrogates. A range of experiments on diversified real-world multiobjective HPO problems (including the HPO of multi-label/multi-task learning models and the HPO of models with several performance metrics) are carried out to investigate the performance of our proposed algorithm. Experimental results showcase that the proposed algorithm outperforms more than 10 state-of-the-art peers, while demonstrating the ability of our proposed algorithm to efficiently solve real-world multiobjective HPO problems at scale.

关键词： hyperparameter optimization Multiobjective optimization Bayesian optimization Hyperband Surrogate

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Language-independent hyperparameter optimization based speech emotion recognition system

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International Journal of Information Technology (Singapore) 2022年第7期14卷 3691-3699页

作者： Thakur, Anuja Dhull, Sanjeev Kumar Guru Jambheshwar University of Science and Technology Hisar 125001 India

Speech emotion recognition is challenging due to substantially overlapping regions of emotions. Extracting desired features that influence emotions in a speech and categorizing these emotions is a tedious task. We intend to develop an effective and robust speech emotion recognition system capable of classifying ambiguous and overlapping emotions through this manuscript. Three feature sets Spectral, Prosodic, and Discrete Wavelet Transform are extracted and further processed to reduce the required combination of features. The use of hyper-parameter optimization in the machine learning model has been done to tune the support vector machine classifier parameter for the Speech emotion recognition system. The suggested model is also verified with two different language datasets: ‘SAVEE’ and ‘EmoDB’ resulting in a language-independent emotion recognition system from speech. The performance result achieved by employing the proposed technique in EmoDB with 535 samples and SAVEE with 480 samples in seven different emotion types is 90.02% and 71.66%, respectively. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Feature extraction hyperparameter optimization Machine learning Speech emotion recognition

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Budget-Aware Scheduling for hyperparameter optimization Process in Cloud Environment 21st

Budget-Aware Scheduling for Hyperparameter Optimization Proc...

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21st International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Yao, Yan Yu, Jiguo Cao, Jian Liu, Zengguang Qilu Univ Technol Sch Comp Sci & Technol Shandong Acad Sci Jinan Peoples R China Qilu Univ Technol Big Data Inst Shandong Acad Sci Jinan Peoples R China Shandong Lab Comp Networks Jinan Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China

ISBN: (纸本)9783030953911;9783030953904

hyperparameter optimization, as a necessary step for majority machine learning models, is crucial to achieving optimal model performance. Unfortunately, the process of hyperparameter optimization is usually computation-intensive and time-consuming due to the large searching space. To date, with the popularity and maturity of cloud computing, many researchers leverage public cloud services (i.e. Amazon AWS) to train machine learning models. Time and monetary cost, two contradictory targets, are what cloud machine learning users are more concerned about. In this paper, we propose HyperWorkflow, a workflow engine service for hyperparameter optimization execution, that coordinates between hyperparameter optimization job and cloud service instances. HyperWorkflow orchestrates the hyperparameter optimization process in a parallel and cost-effective manner upon heterogeneous cloud resources, and schedules hyperparameter trials using bin packing approach to make the best use of cloud resources to speed up the tuning processing under budget constraint. The evaluations show that HyperWorkflow can speed up hyperparameter optimization execution across a range of different budgets.

关键词： hyperparameter optimization Cloud computing Resource scheduling

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A conjugated evolutionary algorithm for hyperparameter optimization

A conjugated evolutionary algorithm for hyperparameter optim...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Japa, Luis Serqueira, Marcello Mendonca, Israel Bezerra, Eduardo Aritsugi, Masayoshi Gonzalez, Pedro Henrique Kumamoto Univ Grad Sch Sci & Technol Kumamoto Japan Fed Ctr Technol Educ Rio de Janeiro Dept Comp Sci Rio De Janeiro Brazil Kumamoto Univ Fac Adv Sci & Technol Kumamoto Japan

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

With the recent upsurge in the use of deep learning and other computationally expensive machine learning models, hyperparameter optimization has become a quite important and widely researched area of study. Genetic algorithms, a subclass of evolutionary algorithms, have proven to be an effective approach and have been widely used in recent years. However, efficiently exploring the domain of possible solutions remains a challenging, and often computationally-expensive task. In this paper, we present a novel and efficient hyperparameter optimization strategy based on a genetic algorithms variant: Biased Random-key Genetic Algorithms (BRKGA). One of the main challenges of BRKGA is its limited capacity to explore the domain surrounding a particular individual. Although good genes will be preserved by its bias property, these genes are copied as they are, and even if a better solution exists in the close neighborhood of a particular gene it might never be explored. We tackle this problem by adding an exploitation component at the end of every evolutionary step, further exploring the hyperparameter domain. Several computational experiments on eight different publicly available datasets were performed to assess the effectiveness of the proposed approach and to prove it is a significant improvement over its predecessor. The results show that our proposed method outperforms, in terms of the F-1 score of the resulting Artificial Neural Network, not only BRKGA but also other commonly used methods in most of the test cases.

关键词： optimization hyperparameter optimization genetic algorithms evolutionary algorithms metaheuristic

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Regularized boosting with an increasing coefficient magnitude stop criterion as meta-learner in hyperparameter optimization stacking ensemble

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NEUROCOMPUTING 2023年第1期551卷

作者： Fdez-Diaz, Laura Quevedo, Jose Ramon Montanes, Elena Global R&D ArcelorMittal Calle Marineros 4 Aviles 33490 Asturias Spain Univ Oviedo Artificial Intelligence Ctr Gijon 33204 Asturias Spain Edificio Departamental Oeste N 1 Despacho 1 B 12C Gijon 33204 Asturias Spain

hyperparameter optimization (HPO) aims to tune hyperparameters for a system in order to improve the predictive performance. Typically, only the hyperparameter configuration with the best performance is chosen after performing several trials. However, some works try to take advantage of the effort made when training all the models with every hyperparameter configuration trial and, instead of discarding all but one, they propose performing an ensemble of all the models. However, this ensemble consists of simply averaging the model predictions or weighting the models by a certain probability. Recently, some of the so-called Automated Machine Learning (AutoML) frameworks have included other more sophisticated ensemble strategies, such as the Caruana method or the stacking strategy. On the one hand, the Caruana method has been shown to perform well in HPO ensemble, since it is not affected by the issues caused by multicollinearity, which is prevalent in HPO. It just computes the average over a subset of predictions, previously chosen through a forward stepwise selection with replacement. But it does not benefit from the generalization power of a learning process. On the other hand, stacking approaches include a learning procedure since a meta-learner is required to perform the ensemble. Yet, one hardly finds advice about which meta-learner can be adequate. Besides, some possible meta-learners may suffer from problems caused by multicollinearity or need to be tuned in order to mitigate or reduce this obstacle. In an attempt to reduce this lack of advice, this paper exhaustively explores possible meta-learners for stacking ensemble in HPO, free of hyperparameter tuning and able to mitigate the problems derived from multicollinearity as well as taking advantage of the generalization power that a learning process may include in the ensemble. Particularly, the boosting strategy shows promise in this context as a stacking meta-learner, since it satisfies the required condition

关键词： hyperparameter optimization Stacking ensemble Boosting

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A surrogate-assisted highly cooperative coevolutionary algorithm for hyperparameter optimization in deep convolutional neural networks

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APPLIED SOFT COMPUTING 2023年 147卷

作者： Chen, An Ren, Zhigang Wang, Muyi Chen, Hui Leng, Haoxi Liu, Shuai Xi An Jiao Tong Univ Sch Automat Sci & Engn Xian Peoples R China Xi An Jiao Tong Univ Sch Automat Sci & Engn 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China

hyperparameter optimization in convolutional neural networks (CNNs) plays a vital role in ensuring the effectiveness of the models. However, with the depth of the existing CNN expanding, this task becomes very challenging due to the high-dimensional and computationally expensive characteristics. Given these difficulties, this study proposes a surrogate-assisted highly cooperative hyperparameter optimization (SHCHO) algorithm for large-scale chain-styled CNNs. SHCHO tackles the original hyperparameter optimization problem by cooperatively optimizing its subproblems. Specifically, it first decomposes the whole CNN into several overlapping sub-CNNs, which conforms to the inherent overlapping interaction structure among hyperparameters and significantly reduces the search space. The resulting hyperparameter optimization subproblems on these sub-CNNs are then coevolved collaboratively and competitively, facilitating a proper hyperparameter configuration for the whole CNN. Besides, SHCHO also employs the computationally efficient surrogate technique to assist in each subproblem optimization. As a result, the expensive computational cost can be greatly reduced. Extensive experimental results on three widely-used image classification datasets indicate that SHCHO shows competitive performance when compared with several state-of-the-art algorithms in terms of both hyperparameter superiority and computational cost.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Convolutional neural networks hyperparameter optimization Divide and conquer Surrogate model

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How to Better Distinguish Security Bug Reports (Using Dual hyperparameter optimization)

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EMPIRICAL SOFTWARE ENGINEERING 2021年第3期26卷 1-37页

作者： Shu, Rui Xia, Tianpei Chen, Jianfeng Williams, Laurie Menzies, Tim North Carolina State Univ Dept Comp Sci Raleigh NC 27695 USA

Background In order that the general public is not vulnerable to hackers, security bug reports need to be handled by small groups of engineers before being widely discussed. But learning how to distinguish the security bug reports from other bug reports is challenging since they may occur rarely. Data mining methods that can find such scarce targets require extensive optimization effort. Goal The goal of this research is to aid practitioners as they struggle to optimize methods that try to distinguish between rare security bug reports and other bug reports. Method Our proposed method, called SWIFT, is a dual optimizer that optimizes both learner and pre-processor options. Since this is a large space of options, SWIFT uses a technique called 𝜖�-dominance that learns how to avoid operations that do not significantly improve performance. Result When compared to recent state-of-the-art results (from FARSEC which is published in TSE'18), we find that the SWIFT's dual optimization of both pre-processor and learner is more useful than optimizing each of them individually. For example, in a study of security bug reports from the Chromium dataset, the median recalls of FARSEC and SWIFT were 15.7% and 77.4%, respectively. For another example, in experiments with data from the Ambari project, the median recalls improved from 21.5% to 85.7% (FARSEC to SWIFT). Conclusion Overall, our approach can quickly optimize models that achieve better recalls than the prior state-of-the-art. These increases in recall are associated with moderate increases in false positive rates (from 8% to 24%, median). For future work, these results suggest that dual optimization is both practical and useful.

关键词： hyperparameter optimization Data pre-processing Security bug report

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Coverage-Based Designs Improve Sample Mining and hyperparameter optimization

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021年第3期32卷 1241-1253页

作者： Muniraju, Gowtham Kailkhura, Bhavya Thiagarajan, Jayaraman J. Bremer, Peer-Timo Tepedelenlioglu, Cihan Spanias, Andreas Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA Lawrence Livermore Natl Lab Ctr Appl Sci Comp Livermore CA 94550 USA

Sampling one or more effective solutions from large search spaces is a recurring idea in machine learning (ML), and sequential optimization has become a popular solution. Typical examples include data summarization, sample mining for predictive modeling, and hyperparameter optimization. Existing solutions attempt to adaptively trade off between global exploration and local exploitation, in which the initial exploratory sample is critical to their success. While discrepancy-based samples have become the de facto approach for exploration, results from computer graphics suggest that coverage-based designs, e.g., Poisson disk sampling, can be a superior alternative. In order to successfully adopt coverage-based sample designs to ML applications, which were originally developed for 2-D image analysis, we propose fundamental advances by constructing a parameterized family of designs with provably improved coverage characteristics and developing algorithms for effective sample synthesis. Using experiments in sample mining and hyperparameter optimization for supervised learning, we show that our approach consistently outperforms the existing exploratory sampling methods in both blind exploration and sequential search with Bayesian optimization.

关键词： optimization Search problems Bayes methods Predictive models Measurement Machine learning Sampling methods Coverage-based sample design hyperparameter optimization Poisson disk sampling (PDS) predictive modeling sequential optimization

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