Novel IPCA-Based Classifiers and Their Application to Spam Filtering

This paper proposes a novel two-class classifier, called IPCAC, based on the isotropic principal component analysis technique; it allows to deal with training data drawn from mixture of Gaussian distributions, by projecting the data on the Fisher subspace that separates the two classes. The obtained results demonstrate that IPCAC is a promising technique; furthermore, to cope with training datasets being dynamically supplied, and to work with non-linearly separable classes, two improvements of this classifier are defined: a model merging algorithm, and a kernel version of IPCAC. The effectiveness of the proposed methods is shown by their application to the spam classification problem, and by the comparison of the achieved results with those obtained by support vector machines SVM, and K-nearest neighbors KNN.