CBA_Optimized: An Optimized Association based Classification Model with Enhanced Classification Accuracy Rate

Abstract

Association classification is now evolving as an efficient data mining technique for building the classification models with enhanced accuracy rate. Many studies proposed different pruning methods to obtain highly efficient classifier rules in order to enhance the accuracy of the classifier. The pruning strategy must lead to reduced number of high quality rules that contribute to the accurate classification of the data. This study presents the improved version of association rule based classification algorithm CBA (Classification based on Association ) called CBA_Optimized that attempts to optimize the rules discovered by rule generation process used in CBA. The CBA_Optimized uses a unique ranking process to order the occurrence of the rules based on high confidence and rule class frequency to select the classifier rule to classify the data. This paper presents the efficient pruning method and extends the unique single rule classification strategy of CBA being more simple and accessible AC algorithms. With reduced size and slightly faster processing speed than other AC algorithms, the study attempts to enhance the classification accuracy rate.