A Fuzzy Fast Classification for Share Market Database with Lower and Upper Bounds
Srinivasan Vaiyapuri, Rajenderan Govind and Vandar Kuzhali Jaganathan
DOI : 10.3844/ajassp.2012.1934.1939
American Journal of Applied Sciences
Volume 9, Issue 12
In recent years, many researchers focused on the research topic of constructing fuzzy classification system. This study introduces a Fuzzy Fast Classification (FFC) approach for large data sets. It has three phases, in the first phase the large data base is reduced with the entropy by removing the number of attribute. In the second phase an approximate classification is obtained by the mean separation of the data by the total weight, upper and lower approximation line is drawn such that 20% of the record lies near the mean line. In the third phase the classification is refined by using fuzzy logic approach for the 20% of the record since they may fall in any one of the category which need to be carefully examined with the degree of fuzzy value. Experimental results for share market database demonstrate that our approach has good classification accuracy while the training is significantly faster than other SVM classifiers. The proposed classifier has distinctive advantages on dealing with huge data sets.
© 2012 Srinivasan Vaiyapuri, Rajenderan Govind and Vandar Kuzhali Jaganathan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.