Evolving data-adaptive support vector machines for binary classification

Dudzik, Wojciech; Nalepa, Jakub; Kawulok, Michał

doi:10.1016/j.knosys.2021.107221

Cited by 29 publications

(10 citation statements)

References 63 publications

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“…Parameters in bold are the ones which are being optimized by FFA. For SVR implementation, Radial basis function (RBF) is chosen due to its generalization capability, handling space complexity problem and non-linear data [ 60 ]. DeepNet is implemented with 3 hidden layers, as complexity level of selected effort estimation datasets is moderate.…”

Section: Methodsmentioning

confidence: 99%

Ensemble effort estimation with metaheuristic hyperparameters and weight optimization for achieving accuracy

Yasmin,

Haider Butt,

Daud

2024

PLoS ONE

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Software development effort estimation (SDEE) is recognized as vital activity for effective project management since under or over estimating can lead to unsuccessful utilization of project resources. Machine learning (ML) algorithms are largely contributing in SDEE domain, particularly ensemble effort estimation (EEE) works well in rectifying bias and subjectivity to solo ML learners. Performance of EEE significantly depends on hyperparameter composition as well as weight assignment mechanism of solo learners. However, in EEE domain, impact of optimization in terms of hyperparameter tunning as well as weight assignment is explored by few researchers. This study aims in improving SDEE performance by incorporating metaheuristic hyperparameter and weight optimization in EEE, which enables accuracy and diversity to the ensemble model. The study proposed Metaheuristic-optimized Multi-dimensional bagging scheme and Weighted Ensemble (MoMdbWE) approach. This is achieved by proposed search space division and hyperparameter optimization method named as Multi-dimensional bagging (Mdb). Metaheuristic algorithm considered for this work is Firefly algorithm (FFA), to get best hyperparameters of three base ML algorithms (Random Forest, Support vector machine and Deep Neural network) since FFA has shown promising results of fitness in terms of MAE. Further enhancement in performance is achieved by incorporating FFA-based weight optimization to construct Metaheuristic-optimized weighted ensemble (MoWE) of individual multi-dimensional bagging schemes. Proposed scheme is implemented on eight frequently utilized effort estimation datasets and results are evaluated by 5 error metrices (MAE, RMSE, MMRE, MdMRE, Pred), standard accuracy and effect size along with Wilcox statistical test. Findings confirmed that the use of FFA optimization for hyperparameter (with search space sub-division) and for ensemble weights, has significantly enhanced performance in comparison with individual base algorithms as well as other homogeneous and heterogenous EEE techniques.

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Section: Methodsmentioning

confidence: 99%

Ensemble effort estimation with metaheuristic hyperparameters and weight optimization for achieving accuracy

Yasmin,

Haider Butt,

Daud

2024

PLoS ONE

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“…The baseline methods used to evaluate the effectiveness of the SEC. Some typical machine learning methods are adopted as parts of baseline methods: 1) Support vector machine (SVM) ( Dudzik et al, 2021 ) projects the features into high dimensionality and finds the optimal hyperplane to generate classifications. 2) Random forest (RF) ( Probst and Boulesteix, 2018 ) builds multiple decision trees via randomly selecting features and generating classification results by tree voting.…”

Section: Materials and Methodologymentioning

confidence: 99%

“…1) Support vector machine (SVM) ( Dudzik et al, 2021 ) projects the features into high dimensionality and finds the optimal hyperplane to generate classifications.…”

Section: Materials and Methodologymentioning

confidence: 99%

Early prediction of atherosclerosis diagnosis with medical ambient intelligence

et al. 2023

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Atherosclerosis is a chronic vascular disease that poses a significant threat to human health. Common diagnostic methods mainly rely on active screening, which often misses the opportunity for early detection. To overcome this problem, this paper presents a novel medical ambient intelligence system for the early detection of atherosclerosis by leveraging clinical data from medical records. The system architecture includes clinical data extraction, transformation, normalization, feature selection, medical ambient computation, and predictive generation. However, the heterogeneity of examination items from different patients can degrade prediction performance. To enhance prediction performance, the “SEcond-order Classifier (SEC)” is proposed to undertake the medical ambient computation task. The first-order component and second-order cross-feature component are then consolidated and applied to the chosen feature matrix to learn the associations between the physical examination data, respectively. The prediction is lastly produced by aggregating the representations. Extensive experimental results reveal that the proposed method’s diagnostic prediction performance is superior to other state-of-the-art methods. Specifically, the Vitamin B12 indicator exhibits the strongest correlation with the early stage of atherosclerosis, while several known relevant biomarkers also demonstrate significant correlation in experimental data. The method proposed in this paper is a standalone tool, and its source code will be released in the future.

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“…The purpose of support vector machines [26][27][28] is to solve the problem of linear binary classification. Their principle is to find the optimal hyperplane that can classify two types of sample in two-dimensional space.…”

Section: Basic Theory Of Svmmentioning

confidence: 99%

A Hybrid Method for the Fault Diagnosis of Onboard Traction Transformers

Zhu

Liu

et al. 2022

Electronics

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As vital equipment in high-speed train power supply systems, the failure of onboard traction transformers affect the safe and stable operation of the trains. To diagnose faults in onboard traction transformers, this paper proposes a hybrid optimization method based on quickly and accurately using support vector machines (SVMs) as fault diagnosis systems for onboard traction transformers, which can accurately locate and analyze faults. Considering the limitations of traditional transformers for identifying faults, this study used kernel principal component analysis (KPCA) to analyze the feature quantity of dissolved gas analysis (DGA) data, electrical test data, and oil quality test data. The improved seagull optimization algorithm (ISOA) was used to optimize the SVM, and a Henon chaotic map was introduced to initialize the population. Combined with differential evolution (DE) based on the adaptive formula, the foraging formula of the seagull optimization algorithm (SOA) was improved to increase the diversity of the algorithm and enhance its ability to find the optimal parameters of SVM, which made the simulation results more accurate. Finally, the KPCA–ADESOA–SVM model was constructed and applied to fault diagnosis for the traction transformer. The example analysis compared the diagnosis results of the proposed diagnosis model with those of the traditional diagnosis model, showing further optimization of the feature quantity and improvements in the diagnosis accuracy. This proves that the proposed diagnosis model has high generalization performance and can effectively increase the fault diagnosis accuracy and speed of traction transformers.

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Evolving data-adaptive support vector machines for binary classification

Cited by 29 publications

References 63 publications

Ensemble effort estimation with metaheuristic hyperparameters and weight optimization for achieving accuracy

Ensemble effort estimation with metaheuristic hyperparameters and weight optimization for achieving accuracy

Early prediction of atherosclerosis diagnosis with medical ambient intelligence

A Hybrid Method for the Fault Diagnosis of Onboard Traction Transformers

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