Using Conjunction of Attribute Values for Classification

Abstract

Advances in the efficient discovery of frequent itemsets in large databases have led to the development of a number of schemes that use frequent itemsets to aid in the development of accurate and efficient classifiers. These approaches use the frequent itemsets to generate a set of composite features that expand the dimensionality of the underlying dataset. In this paper, we build upon this work and (i) present a variety of schemes for composite feature selection that achieve a substantial reduction in the number of features without adversely affecting the accuracy gains, and (ii) show (both analytically and experimentally) that the composite feature space can lead to improved classification models in the context of support vector machines, in which the dimensionality can automatically be expanded by the use of appropriate kernel functions.