Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Data Mining and Knowledge Discovery
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A robust minimax approach to classification
The Journal of Machine Learning Research
Support Vector Data Description
Machine Learning
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Extreme re-balancing for SVMs: a case study
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Feature selection for text categorization on imbalanced data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
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
Considering Cost Asymmetry in Learning Classifiers
The Journal of Machine Learning Research
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
Predicting credit card customer churn in banks using data mining
International Journal of Data Analysis Techniques and Strategies
KEEL: a software tool to assess evolutionary algorithms for data mining problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
A wrapper method for feature selection using Support Vector Machines
Information Sciences: an International Journal
IEEE Transactions on Knowledge and Data Engineering
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Feature Selection with High-Dimensional Imbalanced Data
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Optimization Methods & Software - The International Conference on Engineering Optimization (EngOpt 2008)
Simultaneous feature selection and classification using kernel-penalized support vector machines
Information Sciences: an International Journal
A dynamic over-sampling procedure based on sensitivity for multi-class problems
Pattern Recognition
Active learning and subspace clustering for anomaly detection
Intelligent Data Analysis
An exploration of learning when data is noisy and imbalanced
Intelligent Data Analysis
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
Diversified ensemble classifiers for highly imbalanced data learning and its application in bioinformatics
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Learning from imbalanced data sets is an important machine learning challenge, especially in Support Vector Machines (SVM), where the assumption of equal cost of errors is made and each object is treated independently. Second-order cone programming SVM (SOCP-SVM) studies each class separately instead, providing quite an interesting formulation for the imbalanced classification task. This work presents a novel second-order cone programming (SOCP) formulation, 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 using two margin variables associated with each class. A regularization parameter C is considered in order to control the trade-off between the maximization of these two margin variables. The proposed method has the following advantages: it provides better results, since it is specially designed for imbalanced classification, and it reduces computational complexity, since one conic restriction is eliminated. Experiments on benchmark imbalanced data sets demonstrate that our approach accomplishes the best classification performance, compared with the traditional SOCP-SVM formulation and with cost-sensitive formulations for linear SVM.