Combining classifiers for harmful document filtering

It is presented how learning based methods combined to introduction of a priori knowledge on classifiers enable us to get better filtering performances than classical approaches (such as static black/white lists and single classifier).

Abstract

In this paper, we describe the experiments that we have carried out during the European Research Project NetProtect II that aims at filtering harmful Web pages in order to protect children. These experiments focus on the combination of classifiers (relying on texts, images and addresses), dealing with heterogeneous classes (bomb-making, drug, pornography, violence) for multimedia documents (composed of both semi-structured text and images). We test and compare different combination formulas (Voting methods, logical methods, k Nearest Neighbors, evidence-based k Nearest Neighbors, Naive Bayes, Artificial Neural Network and Support Vector Machine) on a five thousand webpages database. We present how learning based methods combined to introduction of a priori knowledge on classifiers enable us to get better filtering performances than classical approaches (such as static black/white lists and single classifier).