Title :
r-SVMT: Discovering the knowledge of association rule over SVM classification trees
Author :
Pang, Shaoning ; Kasabov, Nik
Author_Institution :
Knowledge Eng. & Discovery Res. Inst, Auckland Univ. of Technol., Auckland
Abstract :
This paper presents a novel method of rule extraction by encoding the knowledge of the data into an SVM classification tree (SVMT), and decoding the trained SVMT into a set of linguistic association rules. The method of rule extraction over the SVMT (r-SVMT), in the spirit of decision-tree rule extraction, achieves rule extraction not only from SVM, but also over the obtained decision-tree structure. The benefits of r-SVMT are that the decision-tree rule provides better comprehensibility, and the support-vector rule retains the good classification accuracy of SVM. Furthermore, the r-SVMT is capable of performing a very robust classification on such datasets that have seriously, even overwhelmingly, class-imbalanced data distribution, which profits from the super generalization ability of SVMT owing to the aggregation of a group of SVMs. Experiments with a gaussian synthetic data, seven benchmark cancers diagnosis have highlighted the utility of SVMT and r-SVMT on encoding and decoding rule knowledge, as well as the superior properties of r-SVMT as compared to a completely support-vector based rule extraction.
Keywords :
computational linguistics; data mining; decision trees; learning (artificial intelligence); pattern classification; support vector machines; tree data structures; data knowledge encoding; decision-tree rule extraction; decision-tree structure; knowledge extraction; linguistic association rule; r-SVMT classification tree; support vector machine; support-vector based rule extraction; Association rules; Classification tree analysis; Data mining; Decision trees; Decoding; Ellipsoids; Encoding; Prototypes; Support vector machine classification; Support vector machines;
Conference_Titel :
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1820-6
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2008.4634145