International Journal of Pharma and Bio Sciences
ijpbs.net
editorijpbs@rediffmail.com (or) editorofijpbs@yahoo.com (or) prasmol@rediffmail.com
10.22376/ijpbs.2019.10.1.p1-12
Volume 5 Issue 3
2014 (July- September)
A NOVEL SUBSET SELECTION FOR CLASSIFICATION OF DIABETE DATASET BY ITERATIVE METHODS
Search methods applied to data mining techniques help us to analyze a data set. These methods are used for reducing the size of the search space in order to select the relevant attribute for identification of diabetes .The research community in diabetes is very much depends on practical prediction and classification of diabetes parameters based on qualified dataset. The main intention in this context is to deal with a large data set with high accuracy. For this purpose models are built using weka tool under supervised learning algorithm. It is necessary to reduce the data dimension before constructing the models and thus the search methods for selection of attributes are followed. Those models are to be applied to predict the possible test cases for evaluation
D.UDHAYAKUMARAPANDIAN, RM. CHANDRASEKARANAND A.KUMARAVEL
: Data mining ,Classification, Diabetes data set, Search Methods , Tree, Meta boost, Bayes
01-08