The nature of statistical learning theory
The nature of statistical learning theory
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Training algorithms for fuzzy support vector machines with noisy data
Pattern Recognition Letters
KBA: Kernel Boundary Alignment Considering Imbalanced Data Distribution
IEEE Transactions on Knowledge and Data Engineering
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
SVMs modeling for highly imbalanced classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Posterior probability support vector Machines for unbalanced data
IEEE Transactions on Neural Networks
A hybrid PSO-FSVM model and its application to imbalanced classification of mammograms
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
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Classification approaches usually present the poor generalization performance with an apparent class imbalance problem. Surely, a measures of the quality of the possible models reflected the remaining uncertainty in the class imbalance on learning. The purpose of our learning method is to lead an attractive pragmatic expansion scheme of the Bayesian approach to assess how well it is aligned with the class imbalance problem. Thus, we propose a method with a model assessment of the interplay between various classification decisions using probability, corresponding decision costs, and quadratic program of optimal margin classifier called: Bayesian Support Vector Machines (BSVMs) learning strategy. In the framework, we did modify in the objects and conditions of primal problem to reproduce an appropriate learning rule for an observation sample. The experiments on several existing data sets showed that BSVMs may appropriately capture the true relationship between the inputs and outputs by experimental evidence.