Improved rule discovery performance on uncertainty

Date

1998-01-01

Author

Tolun, MR
Sever, H

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In this paper we describe the improved version of a novel rule induction algorithm, namely ILA. We first outline the basic algorithm, and then present how the algorithm is enhanced using the new evaluation metric that handles uncertainty in a given data set. In addition to having a faster induction than the original one, we believe that our contribution comes into picture with a new metric that allows users to define their preferences through a penalty factor. We use this penalty factor to tackle with over-fitting bias, which is inherently found in a great many of inductive algorithms. We compare the improved algorithm ILA-2 to a variety of induction algorithms, including ID3, OC1, C4.5, CN2, and ILA. According to our preliminary experimental work, the algorithm appears to be comparable to the well-known algorithms such as CN2 and C4.5 in terms of accuracy and size.

Subject Keywords

Penalty factor, Induction algorithm, Current class, Minimum description length principle, Redundant rule

URI

https://hdl.handle.net/11511/66012

Journal

RESEARCH AND DEVELOPMENT IN KNOWLEDGE DISCOVERY AND DATA MINING

Collections

Department of Computer Engineering, Article

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Citation Formats

M. Tolun and H. Sever, “Improved rule discovery performance on uncertainty,” RESEARCH AND DEVELOPMENT IN KNOWLEDGE DISCOVERY AND DATA MINING, pp. 310–321, 1998, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/66012.