PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Novel IPCA-Based Classifiers and Their Application to Spam Filtering
A. Rozza, Gabriele Lombardi and E. Casiraghi
Proceedings of the 9th International Conference on Intelligent System Design and Applications (ISDA09) 2009.


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 VectorMachines SVM, and K-Nearest Neighbors KNN.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:5733
Deposited By:Elena Casiraghi
Deposited On:08 March 2010