DocumentCode
598649
Title
Experiments on rule induction from incomplete data using three probabilistic approximations
Author
Clark, Patrick G. ; Grzymala-Busse, Jerzy W.
Author_Institution
Department of Electrical Eng. and Computer Sci., University of Kansas, Lawrence, 66045, USA
fYear
2012
fDate
11-13 Aug. 2012
Firstpage
78
Lastpage
83
Abstract
We present results of experiments on rule induction using three probabilistic approximations: lower, middle, and upper. Our results were conducted on four typical series of incomplete data sets with 5% increments of missing attribute values. Two interpretations of missing attribute values were used: lost and “do not care” conditions. We conclude that the best approach (choice of the interpretation of missing attribute values and selection of the best type of approximation) depends on a data set. Probabilistic approximations are constructed from characteristic sets. The number of distinct probabilities associated with characteristic sets is much larger for data sets with “do not care” conditions than with data sets with lost values. Therefore, for data sets with “do not care” conditions the number of probabilistic approximations is also larger.
Keywords
Approximation methods; Data mining; MLEM2 rule induction algorithm; parameterized approximations; probabilistic approximations; rough set theory;
fLanguage
English
Publisher
ieee
Conference_Titel
Granular Computing (GrC), 2012 IEEE International Conference on
Conference_Location
Hangzhou, China
Print_ISBN
978-1-4673-2310-9
Type
conf
DOI
10.1109/GrC.2012.6468610
Filename
6468610
Link To Document