parallel multi-objective genetic algorithms for associative classification rule mining
Association and classification rule mining are two well-known techniques used in data mining. The integrated approach is known as associative classification rule mining (ACRM), which has helped in developing a compact and efficient classifier for the classification of unknown samples. In this paper, we treated the ACRM as a multi-objective problem and applied the Parallel Multi-objective Genetic Algorithms (PMOGAs) to solve it. ACRM is associated with two phases like rule extraction and rule selection. As ACRM is a multi-objective problem so by applying PMOGA on it we can optimize the measures like support and confidence of association rule mining to extract classification rules in rule extraction phase and in rule selection phase a small number of rules are targeted from the extracted rules to design an accurate and compact classifier, which can maximize the accuracy of the rule set and minimize their complexity. Experiments are conducted on UCI data set by using MOGA and PMOGA. Finally the computational results are analyzed and concluded that the PMOGA for multi-objective rule selection generates a Pareto optimal rule sets with a compact set of classification rules in less time vis-a-vis to MOGA without severely degrading their classification accuracy.
multi-objective genetic algorithms association classification associative classifier