Explanation based generalized ε-SVM and its application in intelligent project management

Support vector machine works well in classifying populations characterized by abrupt decreases in density functions. Its generalization accuracy, however, is not always optimal in dealing with real world problems with neither Gaussian distributions nor sharp boundaries. Incorporating domain theory about problems and excellent intelligent techniques in machine learning into SVM becomes one of promising alternatives. A novel approach, explanation based generalized ε-SVM, which synthesizes SVM, prior knowledge, fuzzy logic and neural network, is proposed. Prior knowledge is expressed as a trained fuzzy neural network. An optimal subset of features is obtained by dynamically reducing feature space dimensionality according to the training derivatives extracted from network. By examining a subset of the practical data sampled from Guangdong Natural Science Foundation and testing the remaining set of data, application shows that explanation based generalized ε-SVM performs better than that pure SVM and other traditional classifiers.