Post-evolution of variable-length class prototypes to unlock decision making within support vector machines

Support Vector Machines (SVM) are among the most well-known machine learning methods, with broad use in different scientific areas. However, one necessary pre-processing phase for SVM is normalization (scaling) of features, since SVM is not invariant to the scales of the features’ spaces, i.e., different ways of scaling may lead to different results. We define a more robust decision-making approach for binary classification, in which one sample strongly belongs to a class if it belongs to that class for all possible rescalings of features. We derive a way of characterising the approach for binary SVM that allows determining when an instance strongly belongs to a class and when the classification is invariant to rescaling. The characterisation leads to a computation method to determine whether one sample is strongly positive, strongly negative or neither. Our experimental results back up the intuition that being strongly positive suggests stronger confidence that an instance really is positive.

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Diabetes Diagnosis System Based on Support Vector Machines Trained by Vortex Optimization Algorithm

Nature-Inspired Intelligent Techniques for Solving Biomedical Engineering Problems - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-5225-4769-3.ch009 ◽

2018 ◽

pp. 203-218 ◽

Cited By ~ 3

Author(s):

Sadi Fuat Cankaya ◽

Ibrahim Arda Cankaya ◽

Tuncay Yigit ◽

Arif Koyun

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Support Vector Machines ◽

Optimization Algorithm ◽

Intelligent Systems ◽

Support Vector ◽

Diabetes Diagnosis ◽

Diagnosis System ◽

Vector Machines ◽

Expert Support

Artificial intelligence is widely enrolled in different types of real-world problems. In this context, developing diagnosis-based systems is one of the most popular research interests. Considering medical service purposes, using such systems has enabled doctors and other individuals taking roles in medical services to take instant, efficient expert support from computers. One cannot deny that intelligent systems are able to make diagnosis over any type of disease. That just depends on decision-making infrastructure of the formed intelligent diagnosis system. In the context of the explanations, this chapter introduces a diagnosis system formed by support vector machines (SVM) trained by vortex optimization algorithm (VOA). As a continuation of previously done works, the research considered here aims to diagnose diabetes. The chapter briefly gives information about details of the system and findings reached after using the developed system.

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Modeling medical decision making by support vector machines, explaining by rules of evolutionary algorithms with feature selection

Expert Systems with Applications ◽

10.1016/j.eswa.2012.11.007 ◽

2013 ◽

Vol 40 (7) ◽

pp. 2677-2686 ◽

Cited By ~ 55

Author(s):

Ruxandra Stoean ◽

Catalin Stoean

Keyword(s):

Decision Making ◽

Feature Selection ◽

Support Vector Machines ◽

Evolutionary Algorithms ◽

Medical Decision Making ◽

Medical Decision ◽

Support Vector ◽

Vector Machines

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A support vector machine method for bid/no bid decision making

Journal of Civil Engineering and Management ◽

10.3846/13923730.2017.1281836 ◽

2017 ◽

Vol 23 (5) ◽

pp. 641-649 ◽

Cited By ~ 8

Author(s):

Rifat SONMEZ ◽

Burak SÖZGEN

Keyword(s):

Decision Making ◽

Support Vector Machine ◽

Support Vector Machines ◽

Support Vector Machine Classifier ◽

Oil And Gas ◽

Support Vector ◽

Machine Method ◽

Generalization Performance ◽

Vector Machines ◽

Input Variables

The bid/no bid decision is an important and complex process, and is impacted by numerous variables that are related to the contractor, project, client, competitors, tender and market conditions. Despite the complexity of bid decision making process, in the construction industry the majority of bid/no bid decisions is made informally based on experience, judgment, and perception. In this paper, a procedure based on support vector machines and backward elimination regression is presented for improving the existing bid decision making methods. The method takes advantage of the strong generalization properties of support vector machines and attempts to further enhance generalization performance by eliminating insignificant input variables. The method is implemented for bid/no bid decision making of offshore oil and gas platform fabrication projects to achieve a parsimonious support vector machine classifier. The performance of the support vector machine classifier is compared with the performances of the worth evaluation model, linear regression, and neural network classifiers. The results show that the support vector machine classifier outperforms existing methods significantly, and the proposed procedure provides a powerful tool for bid/no bid decision making. The results also reveal that elimination of the insignificant input variables improves generalization performance of the support vector machines.

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