Attribute Value Taxonomy Generation through Matrix Based Adaptive Genetic Algorithm

We introduce a new adaptive genetic method for AVT generation, MCM-AVT-Learner. The MCM-AVTLearner imports the mutation and crossover matrices which makes effective use of the fitness ranking and loci statistics information. The suggested method is not only parameterfree, but also capable of producing high quality AVTs. We describe experiments on several complete and missing benchmark data sets that compare the performance of AVTDTL using the reslut AVTs of the MCM-AVT-Learner and existing AVT learning algorithms. Results show that the AVTs generated by MCM-AVT-Learner are competitive with human-generated AVTs or AVTs generated by HAC-AVTLearner and GA-AVT-Learner in terms of classification accuracy and the compactness of the classifier.

• Type: Conference
• Year: 2008
• Where: ICTAI
• Authors: Hyunsung Jo, Yong-chan Na, Byonghwa Oh, Jihoon Yang, Vasant Honavar
• Keywords: Adaptive Genetic Method, Artificial Intelligence, AVT Generation, AVT Learning Algorithms, ICTAI 2008