DocumentCode :
2229171
Title :
Rough Set Theory Measures to Knowledge Generation
Author :
Yaile, C. ; Rafael, B. ; Leticia, A. ; Garcia, Mario Macos
Author_Institution :
Univ. of Camaguey, Camaguey
fYear :
2007
fDate :
20-24 Oct. 2007
Firstpage :
401
Lastpage :
406
Abstract :
The accelerated growth of the information volumes on processes, phenomena and reports brings about an increasing interest in the possibility of discovering knowledge from data sets. This is a challenging task because in many cases it deals with extremely large, inherently not structured and fuzzy data, plus the presence of uncertainty. Therefore it is required to know a priori the quality of future procedures without using any additional information. In this paper we propose new measures to evaluate the quality of training sets used by algorithms for learning of supervised classifiers. Our training set assessment relied on measures furnished by rough sets theory. Our experimental results involved three classifiers (k-NN, C-4.5 and MLP) from international data bases. New training sets are built taking into account the results of the measures and the accuracy obtained by the classifiers, with the aim of infer the accuracy that the classifiers would obtain using a new training set. This is possible using a rule generator (C4.5) and a function estimation algorithm (k-NN).
Keywords :
data mining; rough set theory; C-4.5; MLP; function estimation algorithm; fuzzy data; information volumes; k-NN; knowledge discovery; knowledge generation; rough set theory measures; rule generator; supervised classifiers; training set assessment; Acceleration; Application software; Computer science; Intelligent systems; Learning systems; Machine learning; Machine learning algorithms; Rough sets; Set theory; Volume measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Systems Design and Applications, 2007. ISDA 2007. Seventh International Conference on
Conference_Location :
Rio de Janeiro
Print_ISBN :
978-0-7695-2976-9
Type :
conf
DOI :
10.1109/ISDA.2007.62
Filename :
4389641
Link To Document :
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