Multi-objective adaptive differential evolution for SVM/SVR hyperparameters selection

Santos, Carlos Eduardo da Silva; Sampaio, Renato Coral; Coelho, Leandro dos Santos; Bestard, Guillermo Alvarez; Llanos, Carlos H.

doi:10.1016/j.patcog.2020.107649

Cited by 58 publications

(20 citation statements)

References 20 publications

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“…The MSE and MSD values of the F-W-GWO and F-W-DE are similar. Meanwhile, F-W-GWO has a more uniform distribution than F-W-DE because F-W-DE selects continuous bands between [25,50]. F-W-MPA and F-W-IDEA both have high MSEs, but their MSDs are in the middle.…”

Section: Analysis Of the Selected Bands Of The F-w-sieasmentioning

confidence: 99%

“…In terms of the classifier, SVM is now the most commonly used supervised classifier, which can efficiently solve classification problems with small sample sizes and high-dimensional datasets. In particular, many studies [10,[23][24][25][26] have addressed the HSI classification problem by using SVM and obtained superior classification accuracy. Since the FS problem is an NP-hard problem [16], combining an exhaustive search with a classifier is impractical to evaluate all feature subsets except for small-sized feature spaces.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Analysis of Swarm Intelligence and Evolutionary Algorithms for Feature Selection in SVM-Based Hyperspectral Image Classification

Shang

Zheng

et al. 2022

Remote Sensing

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Feature selection (FS) is vital in hyperspectral image (HSI) classification, it is an NP-hard problem, and Swarm Intelligence and Evolutionary Algorithms (SIEAs) have been proved effective in solving it. However, the high dimensionality of HSIs still leads to the inefficient operation of SIEAs. In addition, many SIEAs exist, but few studies have conducted a comparative analysis of them for HSI FS. Thus, our study has two goals: (1) to propose a new filter–wrapper (F–W) framework that can improve the SIEAs’ performance; and (2) to apply ten SIEAs under the F–W framework (F–W–SIEAs) to optimize the support vector machine (SVM) and compare their performance concerning five aspects, namely the accuracy, the number of selected bands, the convergence rate, and the relative runtime. Based on three HSIs (i.e., Indian Pines, Salinas, and Kennedy Space Center (KSC)), we demonstrate how the proposed framework helps improve these SIEAs’ performances. The five aspects of the ten algorithms are different, but some have similar optimization capacities. On average, the F–W–Genetic Algorithm (F–W–GA) and F–W–Grey Wolf Optimizer (F–W–GWO) have the strongest optimization abilities, while the F–W–GWO requires the least runtime among the ten. The F–W–Marine Predators Algorithm (F–W–MPA) is second only to the two and slightly better than F–W–Differential Evolution (F–W–DE). The F–W–Ant Lion Optimizer (F–W–ALO), F–W–I-Ching Divination Evolutionary Algorithm (F–W–IDEA), and F–W–Whale Optimization Algorithm (F–W–WOA) have the middle optimization abilities, and F–W–IDEA takes the most runtime. Moreover, the F–W–SIEAs outperform other commonly used FS techniques in accuracy overall, especially in complex scenes.

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Section: Analysis Of the Selected Bands Of The F-w-sieasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Swarm Intelligence and Evolutionary Algorithms for Feature Selection in SVM-Based Hyperspectral Image Classification

Shang

Zheng

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

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“…SVR has the flexibility of balancing the trade-off between minimizing the empirical error and the complexity of the resulting fitted function, reducing the risk of overfitting. However, SVR is not interpretable and sensitive to missing values; when there are a large number of samples, the efficiency of the SVR is low [26].…”

Section: Support Vector Regression Support Vector Machine Which Was Proposed By Cortes and Vapnikmentioning

confidence: 99%

A Novel Stacking Heterogeneous Ensemble Model with Hybrid Wrapper-Based Feature Selection for Reservoir Productivity Predictions

Zhou¹,

Zhou²,

Liu³

et al. 2021

Complexity

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Acid fracturing is the most important stimulation method in the carbonate reservoir. Due to the high cost and high risk of acid fracturing, it is necessary to predict the reservoir productivity before acid fracturing, which can provide support to optimize the parameters of acid fracturing. However, the productivity of a single well is affected by various construction parameters and geological conditions. Overfitting can occur when performing productivity prediction tasks on the high-dimension, small-sized reservoir, and acid fracturing dataset. Therefore, this study developed a stacking heterogeneous ensemble model with a hybrid wrapper-based feature selection strategy to forecast reservoir productivity, resolve the overfitting problem, and improve productivity prediction. Compared to other baseline models, the proposed model was found to have the best predictive performances on the test set and effectively deal with the overfitting. The results proved that the hybrid wrapper-based feature selection strategy introduced in this study reduced data acquisition costs and improved model comprehensibility without reducing model performance.

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“…For the support vector machine with Gaussian kernel, its performance is affected by the penalty parameter and the Gaussian kernel parameter. How to find an efficient search method to determine the model parameter values and obtain the minimum generalization error of the support vector machine is a key issue in the current research work in this direction [32][33][34][35][36] .…”

Section: Introductionmentioning

confidence: 99%

Pattern Recognition for Partial Discharge Using Multi-Feature Combination Adaptive Boost Classification Model

Yao

Li²,

Hui

et al. 2021

IEEE Access

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This paper proposes a multi-feature combination adaptive boost classification model, considering the difference and complementarity of the three different single feature sets for partial discharge pattern recognition. First, eight types of physical models are designed. Then, an UHF measurement system is used to collect partial discharge data. Second, three kinds of single feature sets extracted from the Phase Resolved Pulse Sequence (PRPS) data are combined with pairs and three to construct new feature sets. The final optimal feature set is selected from the single feature set and the combined feature set as the input of the classification model. Finally, using the boosting algorithm in combination learning to process the training data set, taking the support vector machine as the base classifier, and measuring the inconsistency between one base classifier and other base classifiers by using the "unpaired" diversity index based on information entropy. By this method, a series of various SVM-based classifiers with moderate accuracy are obtained, and finally an adaptive boost classification model based on the multi-feature combination method is obtained. For each defect, 25 samples were obtained at the same test voltage level, and a total of 150 samples were obtained at 6 voltage levels through multiple experiments. The proposed method was compared with the traditional methods using these data sets. The results revealed that the proposed method successfully identified the types of partial discharge insulation defects.

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Multi-objective adaptive differential evolution for SVM/SVR hyperparameters selection

Cited by 58 publications

References 20 publications

A Comparative Analysis of Swarm Intelligence and Evolutionary Algorithms for Feature Selection in SVM-Based Hyperspectral Image Classification

A Comparative Analysis of Swarm Intelligence and Evolutionary Algorithms for Feature Selection in SVM-Based Hyperspectral Image Classification

A Novel Stacking Heterogeneous Ensemble Model with Hybrid Wrapper-Based Feature Selection for Reservoir Productivity Predictions

Pattern Recognition for Partial Discharge Using Multi-Feature Combination Adaptive Boost Classification Model

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