Machine Learning
Credit Scoring and Its Applications
Credit Scoring and Its Applications
On the Distance between Two Ellipsoids
SIAM Journal on Optimization
Information Sciences—Applications: An International Journal
A robust minimax approach to classification
The Journal of Machine Learning Research
Second Order Cone Programming Formulations for Feature Selection
The Journal of Machine Learning Research
Second Order Cone Programming Approaches for Handling Missing and Uncertain Data
The Journal of Machine Learning Research
A wrapper method for feature selection using Support Vector Machines
Information Sciences: an International Journal
On learning algorithm selection for classification
Applied Soft Computing
Information Sciences: an International Journal
Optimization Methods & Software - The International Conference on Engineering Optimization (EngOpt 2008)
Combining uncertainty and imprecision in models of medical diagnosis
Information Sciences: an International Journal
Beyond accuracy, f-score and ROC: a family of discriminant measures for performance evaluation
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Feature selection via dependence maximization
The Journal of Machine Learning Research
A support vector machine-based context-ranking model for question answering
Information Sciences: an International Journal
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This paper presents two novel second-order cone programming (SOCP) formulations that determine a linear predictor using Support Vector Machines (SVMs). Inspired by the soft-margin SVM formulation, our first approach (@x-SOCP-SVM) proposes a relaxation of the conic constraints via a slack variable, penalizing it in the objective function. The second formulation (r-SOCP-SVM) is based on the LP-SVM formulation principle: the bound of the VC dimension is loosened properly using the l"~-norm, and the margin is directly maximized. The proposed methods have several advantages: The first approach constructs a flexible classifier, extending the benefits of the soft-margin SVM formulation to second-order cones. The second method obtains comparable results to the SOCP-SVM formulation with less computational effort, since one conic restriction is eliminated. Experiments on well-known benchmark datasets from the UCI Repository demonstrate that our approach accomplishes the best classification performance compared to the traditional SOCP-SVM formulation, LP-SVM, and to standard linear SVM.