Differential evolution algorithm-based kernel parameter selection for Fukunaga-Koontz Transform subspaces construction

Binol, Hamidullah; Bal, Abdullah; Cukur, Huseyin

doi:10.1117/12.2197924

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…For classification problems, a parameter selection technique for RBF in KFKT is presented by Binol et al [22]. The independent variable of RBF, σ, is not optimized in this study.…”

Section: Data Sets and Parameter Settingsmentioning

confidence: 98%

Target oriented dimensionality reduction of hyperspectral data by Kernel Fukunaga–Koontz Transform

Binol

Ochilov

Alam

et al. 2017

Optics and Lasers in Engineering

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“…For classification problems, a parameter selection technique for RBF in KFKT is presented by Binol et al [22]. The independent variable of RBF, σ, is not optimized in this study.…”

Section: Data Sets and Parameter Settingsmentioning

confidence: 98%

Target oriented dimensionality reduction of hyperspectral data by Kernel Fukunaga–Koontz Transform

Binol

Ochilov

Alam

et al. 2017

Optics and Lasers in Engineering

Self Cite

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“…Namely, the selection of the optimal kernel and parameters is crucial for KPCA to achieve good performance. However, the application results show that no single kernel function can be best for all kinds of machine learning problems [22] and, therefore, learning of optimum kernels over a kernel set is an active research area nowadays [23][24][25][26][27]. Li and Yang presented an ensemble KPCA method with Bayesian inference strategy in [28].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning Based Multiple Kernel Principal Component Analysis for Dimensionality Reduction and Classification of Hyperspectral Imagery

Binol

2018

Mathematical Problems in Engineering

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Classification is one of the most challenging tasks of remotely sensed data processing, particularly for hyperspectral imaging (HSI). Dimension reduction is widely applied as a preprocessing step for classification; however the reduction of dimension using conventional methods may not always guarantee high classification rate. Principal component analysis (PCA) and its nonlinear version kernel PCA (KPCA) are known as traditional dimension reduction algorithms. In a previous work, a variant of KPCA, denoted as Adaptive KPCA (A-KPCA), is suggested to get robust unsupervised feature representation for HSI. The specified technique employs several KPCAs simultaneously to obtain better feature points from each applied KPCA which includes different candidate kernels. Nevertheless, A-KPCA neglects the influence of subkernels employing an unweighted combination. Furthermore, if there is at least one weak kernel in the set of kernels, the classification performance may be reduced significantly. To address these problems, in this paper we propose an Ensemble Learning (EL) based multiple kernel PCA (M-KPCA) strategy. M-KPCA constructs a weighted combination of kernels with high discriminative ability from a predetermined set of base kernels and then extracts features in an unsupervised fashion. The experiments on two different AVIRIS hyperspectral data sets show that the proposed algorithm can achieve a satisfactory feature extraction performance on real data.

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“…At present, studies in the kernel function theory mainly concentrated in three aspects: the first is the properties of the kernel function [7]; the second is the structure (or improved) method of the kernel function [8,9]; the third is the parameter selection of kernel function. [10] There are many kinds of kernel functions, commonly used kernel functions are the following: linear kernel function, polynomial kernel function, radial basis kernel function (RBF) and sigmoid kernel function. In 2003, Keerthi et al [11] studied the RBF kernel and its two involved kernel parameters (penalty parameter c and the kernel width g), and also analyzed the performance of the SVM classifier while kernel parameters took different values; the results showed that if RBF kernel model was used, there was no need to consider the linear support vector machine; Lin et al [12] explained that the sigmoid kernel and RBF kernel had similar performance in certain parameters.…”

Section: Introductionmentioning

confidence: 99%

Biomedical classification application and parameters optimization of mixed kernel SVM based on the information entropy particle swarm optimization

Wang

et al. 2016

Computer Assisted Surgery

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The types of kernel function and relevant parameters' selection in support vector machine (SVM) have a major impact on the performance of the classifier. In order to improve the accuracy and generalization ability of the model, we used mixed kernel function SVM classification algorithm based on the information entropy particle swarm optimization (PSO): on the one hand, the generalization ability of classifier is effectively enhanced by constructing a mixed kernel function with global kernel function and local kernel function; on the other hand, the accuracy of classification is improved through optimization for related kernel parameters based on information entropy PSO. Compared with PSO-RBF kernel and PSO-mixed kernel, the improved PSO-mixed kernel SVM can effectively improve the classification accuracy through the classification experiment on biomedical datasets, which would not only prove the efficiency of this algorithm, but also show that the algorithm has good practical application value in biomedicine prediction.

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Differential evolution algorithm-based kernel parameter selection for Fukunaga-Koontz Transform subspaces construction

Cited by 6 publications

References 7 publications

Target oriented dimensionality reduction of hyperspectral data by Kernel Fukunaga–Koontz Transform

Target oriented dimensionality reduction of hyperspectral data by Kernel Fukunaga–Koontz Transform

Ensemble Learning Based Multiple Kernel Principal Component Analysis for Dimensionality Reduction and Classification of Hyperspectral Imagery

Biomedical classification application and parameters optimization of mixed kernel SVM based on the information entropy particle swarm optimization

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