Title :
Obtaining best parameter values for accurate classification
Author :
Coenen, Frans ; Leng, Paul
Author_Institution :
Dept. of Comput. Sci., The Univ. of Liverpool, UK
Abstract :
In this paper we examine the effect that the choice of support and confidence thresholds has on the accuracy of classifiers obtained by classification association rule mining. We show that accuracy can almost always be improved by a suitable choice of threshold values, and we describe a method for finding the best values. We present results that demonstrate this approach can obtain higher accuracy without the need for coverage analysis of the training data.
Keywords :
data mining; pattern classification; accurate classification; best parameter value; classification association rule mining; confidence threshold; Association rules; Computer science; Costs; Data mining; Machine learning; Smoothing methods; Software testing; Training data;
Conference_Titel :
Data Mining, Fifth IEEE International Conference on
Print_ISBN :
0-7695-2278-5
DOI :
10.1109/ICDM.2005.105
