A new algorithm for data discretization and feature selection

Authors:
Marcela Xavier Ribeiro;Agma J. M. Traina;Caetano Traina, Jr.
Affiliations:
University of São Paulo at São Carlos - Brazil;University of São Paulo at São Carlos - Brazil;University of São Paulo at São Carlos - Brazil
Venue:
Proceedings of the 2008 ACM symposium on Applied computing
Year:
2008

Citing 2
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Abstract

Data discretization and feature selection are two important tasks that can be performed prior to the learning phase of data mining algorithms and can significantly reduce the processing effort of the learning algorithm. In this paper, we present a new algorithm, called Omega, for data preprocessing. Our proposed algorithm performs simultaneously data discretization and feature selection. Some experiments were performed to validate the effects of the preprocessing performed by the Omega algorithm in the results of the C4.5 algorithm (a well-known decision tree-based classifier). The results indicates that the proposed algorithm Omega is well-suited to both, data discretization and feature selection, being appropriate for data pre-processing.