Critical support vector machine without kernel function

Raicharoen, Thanapant; Lursinsap, Chidchanok

doi:10.1109/iconip.2002.1201951

“…One of the earliest works reported in a disease prediction in 2002, [42], used critical SVM without kernel function to a number of benchmark datasets. The proposed algorithm is applied on PIDD as well where reported accuracy is 82.3% without any cross-validation on the PIDD dataset.…”

Section: Disease Prediction Using Other Learnersmentioning

confidence: 99%

Auto-MeDiSine: an auto-tunable medical decision support engine using an automated class outlier detection method and AutoMLP

Jahangir

¹

,

Afzal

²

,

Ahmed

³

et al. 2019

Neural Comput & Applic

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With advanced data analysis techniques, efforts for more accurate decision support systems for disease prediction are on the rise. According to the World Health Organization, diabetes-related illnesses and mortalities are on the rise. Hence, early diagnosis is particularly important. In this paper, we present a framework, Auto-MeDiSine, that comprises an automated version of enhanced class outlier detection using a distance-based algorithm (AutoECODB), combined with an ensemble of automatic multilayer perceptron (AutoMLP). AutoECODB is built upon ECODB by automating the tuning of parameters to optimize outlier detection process. AutoECODB cleanses the dataset by removing outliers. Preprocessed dataset is then used to train a prediction model using an ensemble of AutoMLPs. A set of experiments is performed on publicly available Pima Indian Diabetes Dataset as follows: (1) Auto-MeDiSine is compared with other state-of-the-art methods reported in the literature where Auto-MeDiSine realized an accuracy of 88.7%; (2) AutoMLP is compared with other learners including individual (focusing on neural networkbased learners) and ensemble learners; and (3) AutoECODB is compared with other preprocessing methods. Furthermore, in order to validate the generality of the framework, Auto-MeDiSine is tested on another publicly available BioStat Diabetes Dataset where it outperforms the existing reported results, reaching an accuracy of 97.1%.

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“…In a support vector machine (SVM), the input space is mapped into a highdimensional feature space, and since the mapping function is not explicitly treated by the kernel trick, usually the SVM is trained in the dual form. And many training methods have been developed [1][2][3][4][5][6][7][8][9]. But because the coefficient vector of the hyperplane is expressed by the kernel expansion, substituting the kernel expansion to the coefficient vector, the SVM in the primal form can be solvable.…”

Section: Introductionmentioning

confidence: 99%

Is Primal Better Than Dual

Abe

¹

2009

Artificial Neural Networks – ICANN 2009

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Chapelle proposed to train support vector machines (SVMs) in the primal form by Newton's method and discussed the advantages of training SVMs in the primal. In this paper we propose training L2 SVMs in the dual form in the similar way that Chapelle proposed. Namely, we solve the quadratic programming problem for the initial working set of training data by Newton's method, delete from the working set the data with negative Lagrange multipliers as well as the data with the associated margins larger than or equal to 1, add to the working set training data with the associated margins less than 1, and repeat training the SVM until the working set does not change. The matrix associated with the dual quadratic form is positive definite while that with the primal quadratic form is positive semi-definite. And the former matrix requires less kernel evaluation. Computer experiments show that for most cases training the SVM by the proposed method is more stable and faster than training the SVM in the primal.

show abstract

“…In a support vector machine (SVM), the input space is mapped into a highdimensional feature space, and since the mapping function is not explicitly treated by the kernel trick, usually the SVM is trained in the dual form. And many training methods have been developed [1][2][3][4][5][6][7][8][9]. But because the coefficient vector of the hyperplane is expressed by the kernel expansion, substituting the kernel expansion to the coefficient vector, the SVM in the primal form can be solvable.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks – ICANN 2009

Alippi¹,

Polycarpou²,

Panayiotou³

et al. 2009

Lecture Notes in Computer Science

3

0

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Chapelle proposed to train support vector machines (SVMs) in the primal form by Newton's method and discussed the advantages of training SVMs in the primal. In this paper we propose training L2 SVMs in the dual form in the similar way that Chapelle proposed. Namely, we solve the quadratic programming problem for the initial working set of training data by Newton's method, delete from the working set the data with negative Lagrange multipliers as well as the data with the associated margins larger than or equal to 1, add to the working set training data with the associated margins less than 1, and repeat training the SVM until the working set does not change. The matrix associated with the dual quadratic form is positive definite while that with the primal quadratic form is positive semi-definite. And the former matrix requires less kernel evaluation. Computer experiments show that for most cases training the SVM by the proposed method is more stable and faster than training the SVM in the primal.

show abstract

Critical support vector machine without kernel function

Cited by 16 publications

References 14 publications

Auto-MeDiSine: an auto-tunable medical decision support engine using an automated class outlier detection method and AutoMLP

Auto-MeDiSine: an auto-tunable medical decision support engine using an automated class outlier detection method and AutoMLP

Is Primal Better Than Dual

Artificial Neural Networks – ICANN 2009

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