A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Optimizing classifiers for imbalanced training sets
Proceedings of the 1998 conference on Advances in neural information processing systems II
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Support Vector Machines and the Bayes Rule in Classification
Data Mining and Knowledge Discovery
A Unified Framework for Regularization Networks and Support Vector Machines
A Unified Framework for Regularization Networks and Support Vector Machines
Support Vector Machines and the Bayes Rule in Classification
Data Mining and Knowledge Discovery
KBA: Kernel Boundary Alignment Considering Imbalanced Data Distribution
IEEE Transactions on Knowledge and Data Engineering
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning concepts from large scale imbalanced data sets using support cluster machines
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Asymptotic Bayesian generalization error when training and test distributions are different
Proceedings of the 24th international conference on Machine learning
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Support vector machines with adaptive Lq penalty
Computational Statistics & Data Analysis
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
Statistically undetectable jpeg steganography: dead ends challenges, and opportunities
Proceedings of the 9th workshop on Multimedia & security
Perceptron and SVM learning with generalized cost models
Intelligent Data Analysis
Risk-sensitive loss functions for sparse multi-category classification problems
Information Sciences: an International Journal
Covariate Shift Adaptation by Importance Weighted Cross Validation
The Journal of Machine Learning Research
No-reference image quality assessment using modified extreme learning machine classifier
Applied Soft Computing
Advances in Artificial Intelligence
Proceedings of the international conference on Multimedia information retrieval
Knowledge transfer for cross domain learning to rank
Information Retrieval
Expert Systems with Applications: An International Journal
A theoretical framework for multi-sphere support vector data description
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Maximum Likelihood in Cost-Sensitive Learning: Model Specification, Approximations, and Upper Bounds
The Journal of Machine Learning Research
Multiple distribution data description learning algorithm for novelty detection
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Clustering based bagging algorithm on imbalanced data sets
IUKM'11 Proceedings of the 2011 international conference on Integrated uncertainty in knowledge modelling and decision making
Large Margin Hierarchical Classification with Mutually Exclusive Class Membership
The Journal of Machine Learning Research
Fuzzy support vector machine and its application to mechanical condition monitoring
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
A novel parameter refinement approach to one class support vector machine
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Fuzzy regularized generalized eigenvalue classifier with a novel membership function
Information Sciences: an International Journal
Training and assessing classification rules with imbalanced data
Data Mining and Knowledge Discovery
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The majority of classification algorithms are developed for the standard situation in which it is assumed that the examples in the training set come from the same distribution as that of the target population, and that the cost of misclassification into different classes are the same. However, these assumptions are often violated in real world settings. For some classification methods, this can often be taken care of simply with a change of threshold; for others, additional effort is required. In this paper, we explain why the standard support vector machine is not suitable for the nonstandard situation, and introduce a simple procedure for adapting the support vector machine methodology to the nonstandard situation. Theoretical justification for the procedure is provided. Simulation study illustrates that the modified support vector machine significantly improves upon the standard support vector machine in the nonstandard situation. The computational load of the proposed procedure is the same as that of the standard support vector machine. The procedure reduces to the standard support vector machine in the standard situation.