Journal ArticleDOI
MC2ESVM: Multiclass Classification Based on Cooperative Evolution of Support Vector Machines
Alejandro Rosales-Pérez,Salvador García,Hugo Terashima-Marín,Carlos A. Coello Coello,Francisco Herrera +4 more
TLDR
The optimization problem is reformulated such that it focuses on learning the support vectors for each class at the time that it takes into account the information from other classes, and the effectiveness of MC2ESVM, an approach for multiclass classification based on the cooperative evolution of SVMs is shown.Abstract:
Support vector machines (SVMs) are one of the most powerful learning algorithms for solving classification problems. However, in their original formulation, they only deal with binary classification. Traditional extensions of the binary SVMs for multiclass problems are based either on decomposing the problem into a number of binary classification problems, which are then independently solved, or on reformulating the objective function by solving larger optimization problems. In this paper, we propose MC2ESVM, an approach for multiclass classification based on the cooperative evolution of SVMs. Cooperative evolution allows us to decompose an M-class problem into M subproblems, which are simultaneously optimized in a cooperative fashion. We have reformulated the optimization problem such that it focuses on learning the support vectors for each class at the time that it takes into account the information from other classes. A comprehensive experimental study using common benchmark datasets is carried out to validate MC2ESVM. The experimental results, supported by statistical tests, show the effectiveness of MC2ESVM for solving multiclass classification problems, while keeping a reasonable number of support vectors.read more
Citations
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Journal ArticleDOI
A memetic algorithm using emperor penguin and social engineering optimization for medical data classification
TL;DR: A memetic algorithm-based SVM (M-SVM) is presented for simultaneous feature selection and optimization of SVM parameters and experimental results confirm that the proposed method significantly outperforms other existing techniques in terms of accuracy and number of selected genes.
Journal ArticleDOI
Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support: three decades' development course and future prospect.
TL;DR: In this paper, the authors provide a comprehensive state-of-the-art review of the computer-assisted nodules detection and benign-malignant classification techniques developed over three decades, which have evolved from the complicated ad hoc analysis pipeline of conventional approaches to the simplified seamlessly integrated deep learning techniques.
Journal ArticleDOI
Extended Karush-Kuhn-Tucker Condition for Constrained Interval Optimization Problems and its Application in Support Vector Machines
TL;DR: An extended Karush-Kuhn-Tucker condition is presented to characterize efficient solutions to constrained interval optimization problems and it is observed that these optimality conditions appear with inclusion relations instead of equations.
Journal ArticleDOI
A Data-Driven Approach for Twitter Hashtag Recommendation
TL;DR: The proposed PM-HRec outperforms the existing state of the art hashtag recommendation approaches in terms of quality of recommended hashtags and runtime processing.
Journal ArticleDOI
Deep convolutional neural network architecture design as a bi-level optimization problem
TL;DR: The main contribution behind the work consists in the fact that CNN architecture design has a hierarchical nature and thus could be seen as a Bi-Level Optimization Problem (BLOP) where the upper level minimizes the network complexity and the lower level optimizes the convolution block ‘graphs’ topologies.
References
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James Bergstra,Yoshua Bengio +1 more
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Journal ArticleDOI
A comparison of methods for multiclass support vector machines
Hsu Chih-Wei,Chih-Jen Lin +1 more
TL;DR: Decomposition implementations for two "all-together" multiclass SVM methods are given and it is shown that for large problems methods by considering all data at once in general need fewer support vectors.