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An hybrid SVM-symbolic architecture for classification tasks is proposed in this work. The learning system relies on a support vector machine (SVM), meanwhile a rule extraction module translate the embedded knowledge in the trained SVM in the form of symbolic rules. The new representation is useful to understand the nature of the problem and its solution.(More)
In this article, we propose some methods for deriving symbolic interpretation of data in the form of rule based learning systems by using Support Vector Machines (SVM). First, Radial Basis Function Neural Networks (RBFNN) learning techniques are explored, as is usual in the literature, since the local nature of this paradigm makes it a suitable platform for(More)
Standard learning algorithms may perform poorly when learning from unbalanced datasets. Based on the Fisher’s discriminant analysis, a post-processing strategy is introduced to deal datasets with significant imbalance in the data distribution. A new bias is defined, which reduces skew towards the minority class. Empirical results from experiments for a(More)
Support vector machines (SVMs) are learning systems based on the statistical learning theory, which are exhibiting good generalization ability on real data sets. Nevertheless, a possible limitation of SVM is that they generate black box models. In this work, a procedure for rule extraction from support vector machines is proposed: the SVM+Prototypes method.(More)
In this work, a procedure for rule extraction from radial basis function (RBFN) networks is proposed. The algorithm is based on the use of a support vector machine (SVM) as a frontier pattern selector. By using geometric methods, centers of the RBF units are combined with support vectors in order to construct regions (ellipsoids or hyper-rectangles) in the(More)