The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Accurately learning from few examples with a polyhedral classifier
Computational Optimization and Applications
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Incorporating Fuzzy Membership Functions into the Perceptron Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy support vector machines based on spherical regions
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Fuzzy support vector machines based on λ-cut
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
A new fuzzy support vector machine to evaluate credit risk
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
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In the context of classification most efforts have been devoted to deriving accurate prediction models from a set of examples whose class is supposed to be known with certainty. However, there are situations where class labels are affected by an intrinsic vagueness, as in ranking customers for marketing campaigns or credit approval. In this paper we propose a new two-phase fuzzy classification method aimed at generating accurate classification rules when labels are uncertain. In the first phase, an ensemble method is applied in order to derive the value of the class membership function for each example in the dataset. In the second phase, an optimal classification model is obtained by solving a fuzzy variant of discrete support vector machines. Computational tests performed on benchmark and real world marketing and credit risk datasets show the effectiveness of the proposed method when it is compared to alternative classification techniques. Furthermore, the tests reveal that the new fuzzy discrete SVM model is a robust regularization method capable of generating stable classification rules, reducing the variance of the error and smoothing out the noise due to outliers.