Multi-Objective Optimization of Support Vector Machines

Suttorp, Thorsten; Igel, Christian

doi:10.1007/11399346_9

Cited by 12 publications

(15 citation statements)

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“…In Figure 4 , the receiver operating characteristics (ROCs) of the classifiers are shown. For the SVMs, the curves were obtained by simply varying the threshold parameter b [ 26 ]. For the Markov chain model, a threshold parameter b was introduced and adjusted, that is, a sequence was classified based on the sign of ln

( s ) - ln

( s ) + b .…”

Section: Resultsmentioning

confidence: 99%

Automatic detection of exonic splicing enhancers (ESEs) using SVMs

et al. 2008

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Background: Exonic splicing enhancers (ESEs) activate nearby splice sites and promote the inclusion (vs. exclusion) of exons in which they reside, while being a binding site for SR proteins. To study the impact of ESEs on alternative splicing it would be useful to have a possibility to detect them in exons. Identifying SR protein-binding sites in human DNA sequences by machine learning techniques is a formidable task, since the exon sequences are also constrained by their functional role in coding for proteins.

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( s ) - ln

( s ) + b .…”

Section: Resultsmentioning

confidence: 99%

Automatic detection of exonic splicing enhancers (ESEs) using SVMs

et al. 2008

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“…Suttorp and Igel () employed multi‐objective evolutionary algorithms to optimize the performance of the well‐known support vector machines (Castillo, Peteiro‐Barral, Guijarro Berdinas, & Fontenla‐Romero, ; Chou & Pham, ; Khedher et al, ). W. Li, Liu, and Gong () developed a multi‐objective uniform design tool, where the gradient‐based search is replaced by a uniform design method to reduce the model selection computational cost as well as to improve the classification accuracy.…”

Section: Meta‐heuristic Multi‐ and Many‐objective Algorithms Applied mentioning

confidence: 99%

Meta‐heuristic multi‐ and many‐objective optimization techniques for solution of machine learning problems

2017

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Recently, multi‐ and many‐objective meta‐heuristic algorithms have received considerable attention due to their capability to solve optimization problems that require more than one fitness function. This paper presents a comprehensive study of these techniques applied in the context of machine learning problems. Three different topics are reviewed in this work: (a) feature extraction and selection, (b) hyper‐parameter optimization and model selection in the context of supervised learning, and (c) clustering or unsupervised learning. The survey also highlights future research towards related areas.

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“…The three-objective optimization model coupled with L 1 − norm SVM presents a significant improvement in the performance of the classifier. Principles of multi-objective optimization have been widely implemented in several applications [18][19][20]. The primary objective of this paper is to provide a reliable approach that can efficiently and accurately classify imbalanced data sets encountered in real-life and large-scale problems.…”

Section: Introductionmentioning

confidence: 99%

Imbalanced Learning with Parametric Linear Programming Support Vector Machine for Weather Data Application

Jafarigol

Trafali̇s

2020

SN COMPUT. SCI.

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Imbalanced learning is a challenging task in predictive modeling and machine learning that has inspired many researchers to attempt to improve the existing algorithms for more accurate predictions of imbalanced data sets. Due to the nature of rare events, developing reliable and efficient classification models for imbalanced data has not been easily accomplished, and over the past two decades, various methods have been proposed. To this end, we propose a Linear Programming Support Vector Machine (LP-SVM) model to address the issue of imbalanced learning in weather applications. To further improve the model's predictive accuracy, we have implemented a parameter selection method based on the multi-objective parametric simplex approach for parameter tuning of LP-SVM. For numerical tests, we have used a real data set consisting of weather observations made by the Bureau of Meteorology's (BM) system in Australia. The results obtained from training and testing the model demonstrate the effectiveness of our proposed model on the tested examples.

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Multi-Objective Optimization of Support Vector Machines

Cited by 12 publications

References 0 publications

Automatic detection of exonic splicing enhancers (ESEs) using SVMs

Automatic detection of exonic splicing enhancers (ESEs) using SVMs

Meta‐heuristic multi‐ and many‐objective optimization techniques for solution of machine learning problems

Imbalanced Learning with Parametric Linear Programming Support Vector Machine for Weather Data Application

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