Prediction Rule Discovery Based on Dynamic Bias Selection

Authors:
Einoshin Suzuki;Toru Ohno
Affiliations:
-;-
Venue:
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Year:
1999

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Abstract

This paper presents an algorithm for discovering prediction rules with dynamic bias selection. A prediction rule, which is aimed at predicting the class of an unseen example, deserves special attention due to its usefulness. However, little attention has been paid to the dynamic selection of biases in prediction rule discovery. A dynamic selection of biases is useful since it reduces humans' burden of choosing and adjusting multiple mining algorithms. In this paper, we propose a novel rule discovery algorithm D3BiS, which is based on a data-driven criterion. Our approach has been validated using 17 data sets.