High-dimensional data mining in finance by Robust semi-supervised Kernel classifiers on maximum covariance discriminant subspace

Kernel machines (such as support vector machines) have demon-strated excellent performance in numerous areas of pattern recognition. However, traditional kernel machines do not make efficient use of both labeled training data and unlabeled testing data. Moreover, high-dimensional and nonlinear distributed data generally degrade the performance of a classifier due to the curse of dimensionality, especially in financial distress predictions. To address these problems, this study proposes a novel hybrid classifier which constructs a robust semi-supervised support vector machine SVM on kernel partial least square discriminant space (KPLSDS). KPLSDS is created by optimal projection of original data space to a low-dimensional subspace which has maximum covariance between inputs and outputs. Robust semi-supervised SVMs constructed on KPLSDS exploit the candidate low-density separators and simultaneously prevent the identification of a poor separator with the help of unlabeled data. Compared with other dimensionality reduction methods and conventional classifiers, the hybrid classifier performs best.