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Attribute Value Taxonomy Generation through Matrix Based Adaptive Genetic Algorithm

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5 Author(s)
Hyunsung Jo ; Data Min. Res. Lab., Sogang Univ., Seoul ; Yong-chan Na ; Byonghwa Oh ; Jihoon Yang
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We introduce a new adaptive genetic method for AVT generation, MCM-AVT-Learner. The MCM-AVT-Learner imports the mutation and crossover matrices which makes effective use of the fitness ranking and loci statistics information. The suggested method is not only parameter-free, but also capable of producing high quality AVTs. We describe experiments on several complete and missing benchmark data sets that compare the performance of AVT-DTL 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-AVT-Learner and GA-AVT-Learner in terms of classification accuracy and the compactness of the classifier.

Published in:

2008 20th IEEE International Conference on Tools with Artificial Intelligence  (Volume:1 )

Date of Conference:

3-5 Nov. 2008