Complexity of Rule Sets Induced from Incomplete Data with Attribute-concept Values and "Do Not Care" Conditions

Patrick G. Clark, Jerzy W. Grzymala-Busse

Abstract

In this paper we study the complexity of rule sets induced from incomplete data sets with two interpretations of missing attribute values: attribute-concept values and “do not care” conditions. Experiments are conducted on 176 data sets, using three kinds of probabilistic approximations (lower, middle and upper) and the MLEM2 rule induction system. The goal of our research is to determine the interpretation and approximation that produces the least complex rule sets. In our experiment results, the size of the rule set is smaller for attribute-concept values for 12 combinations of the type of data set and approximation, for one combination the size of the rule sets is smaller for “do not care” conditions and for the remaining 11 combinations the difference in performance is statistically insignificant (5% significance level). The total number of conditions is smaller for attribute-concept values for ten combinations, for two combinations the total number of conditions is smaller for “do not care” conditions, while for the remaining 12 combinations the difference in performance is statistically insignificant. Thus, we may claim that attribute-concept values are better than “do not care” conditions in terms of rule complexity.

References

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Paper Citation


in Harvard Style

Clark P. and Grzymala-Busse J. (2014). Complexity of Rule Sets Induced from Incomplete Data with Attribute-concept Values and "Do Not Care" Conditions . In Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA, ISBN 978-989-758-035-2, pages 56-63. DOI: 10.5220/0005003400560063


in Bibtex Style

@conference{data14,
author={Patrick G. Clark and Jerzy W. Grzymala-Busse},
title={Complexity of Rule Sets Induced from Incomplete Data with Attribute-concept Values and "Do Not Care" Conditions},
booktitle={Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA,},
year={2014},
pages={56-63},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005003400560063},
isbn={978-989-758-035-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA,
TI - Complexity of Rule Sets Induced from Incomplete Data with Attribute-concept Values and "Do Not Care" Conditions
SN - 978-989-758-035-2
AU - Clark P.
AU - Grzymala-Busse J.
PY - 2014
SP - 56
EP - 63
DO - 10.5220/0005003400560063