Title :
A New Measure Based in the Rough Set Theory to Estimate the Training Set Quality
Author :
Caballero, Yailé ; Bello, Rafael ; Taboada, Alberto ; Nowé, Ann ; García, María M. ; Casas, Gladys
Author_Institution :
Univ. de Camaguey
Abstract :
Due to the wide availability of huge amounts of data in electronic forms, the necessity of turning such data into useful knowledge has increased. This is a proposal of learning from examples. In this paper, we propose measures to evaluate the quality of training sets used by algorithms for learning classification. Our training set assessment relies on measures provided by rough sets theory. Our experimental results involved three classifiers (k-NN, C-4.5 and MLP) applied to international data bases. The new measure we propose shows good results on these test cases
Keywords :
learning by example; multilayer perceptrons; rough set theory; C-4.5; electronic data; k-NN; learning classification; learning from examples; multilayer perceptrons; rough set theory; training set quality; Classification algorithms; Estimation theory; Learning systems; Machine learning; Multilayer perceptrons; Network topology; Rough sets; Set theory; Testing; Turning;
Conference_Titel :
Symbolic and Numeric Algorithms for Scientific Computing, 2006. SYNASC '06. Eighth International Symposium on
Conference_Location :
Timisoara
Print_ISBN :
0-7695-2740-X
DOI :
10.1109/SYNASC.2006.6