DISCRIMINANT ANALYSIS IMPLEMENTATION BASED ON VARIABLE PREDICTIVE MODELS FOR SIMILARITY PATTERN CLASSIFICATION - Saikhu, Ahmad; Eka Putra, Deneng (Proceedings of KNASTIK, 2012)
At present, there are many pattern classification methods that can be used such asLDA, kNN, Bayesian networks, CART, ANN and SVM. However, many classificationmethods mentioned above causes some issues. The problem are the large computationalcost, and weakness of methods mentioned above to classify the class because it is basedsolely on inter-class boundary (decision boundaries).As an alternative method otherthan the methods have already been exist, the relationship between features (interrelation)in a class can be used to classify a sample of a particular class. Based onthese ideas variable predictive model method based class discrimination (VPMCD) isproposed by (Raghuraj &Lakshminarayanan, 2008) as a new classification approach tothe problem of large data and overlapping which cannot be easily solved by the otherclassification methods.The testings are done using six well studied data sets (Diabetic,Hear, Iris, Wine, Digit, Letter). The results are equations wihich have capability toclasify new sample.