Attribute selection based subset generation on Lazy Learning Associative Classification
Lazy learning associative classification method gains higher accuracy than counterpart eager method. But it generates more number of the subset generation. The proposed method overcome this problem by focusing on the important feature of the given test instance. This focused feature from the test tuple play a critical role in finding the main objective. The proposed gain ratio based attribute selection algorithm select the significant fields to bring out most important small subset rules that may accurately predict class label. For example, this will help the physician to treat the patient with the actual condition of the disease from the predictive measures of classification. Experiment results showing that the feature selection based lazy method greatly improve the accuracy and minimize the execution time than eager associative classification. Experiment results show that the proposed algorithm attains better accuracy and reduce the computation time than traditional lazy learning associative classification.