Abstract
Advances in the efficient discovery of frequent itemsets have led to the development of a number of schemes that use frequent itemsets to aid developing 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 features can lead to improved classification models even in the context of support vector machines, in which the dimensionality can automatically be expanded by the use of appropriate kernel functions.
Original language | English (US) |
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Title of host publication | International Conference on Information and Knowledge Management, Proceedings |
Editors | K Kalpakis, N Goharian, D Grossman |
Pages | 356-364 |
Number of pages | 9 |
State | Published - Dec 1 2002 |
Event | Proceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM 2002) - McLean, VA, United States Duration: Nov 4 2002 → Nov 9 2002 |
Other
Other | Proceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM 2002) |
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Country/Territory | United States |
City | McLean, VA |
Period | 11/4/02 → 11/9/02 |
Keywords
- Association rules
- Classification
- Conjunctive attributes
- Feature selection
- SVM