Title :
A Classifier Capable of Handling New Attributes
Author :
Seo, Dong-Hun ; Song, Chi-Hwa ; Lee, Won Don
Author_Institution :
Dept. of Comput. Sci. & Eng., ChungNam Nat. Univ.
fDate :
March 1 2007-April 5 2007
Abstract :
During knowledge acquisition, a new attribute can be added at any time. In such a case, rule generated by the training data with the former attribute set can not be used. Moreover, the rule can not be combined with the new data set with the newly added attribute(s) using the existing algorithms. In this paper, we propose further development of the new inference engine, UChoo, that can handle the above case naturally. Rule generated from the former data set can be combined with the new data set to form the refined rule. This paper shows how this can be done consistently by the extended data expression, and also shows the experimental result to claim the effectiveness of the algorithm
Keywords :
inference mechanisms; knowledge acquisition; pattern classification; UChoo inference engine; attribute set; extended data expression; knowledge acquisition; rule generation; Classification algorithms; Computer science; Data mining; Decision trees; Electronic mail; Engines; Inference algorithms; Pervasive computing; Sensor phenomena and characterization; Training data;
Conference_Titel :
Computational Intelligence and Data Mining, 2007. CIDM 2007. IEEE Symposium on
Conference_Location :
Honolulu, HI
Print_ISBN :
1-4244-0705-2
DOI :
10.1109/CIDM.2007.368891
