Attribute value taxonomy generation through matrix based adaptive genetic algorithm

Hyunsung Jo, Yong Chan Na, Byonghwa Oh, Jihoon Yang, Vasant Honavar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08
Pages393-400
Number of pages8
DOIs
StatePublished - Dec 22 2008
Event20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08 - Dayton, OH, United States
Duration: Nov 3 2008Nov 5 2008

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume1
ISSN (Print)1082-3409

Other

Other20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08
CountryUnited States
CityDayton, OH
Period11/3/0811/5/08

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Computer Science Applications

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  • Cite this

    Jo, H., Na, Y. C., Oh, B., Yang, J., & Honavar, V. (2008). Attribute value taxonomy generation through matrix based adaptive genetic algorithm. In Proceedings - 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08 (pp. 393-400). [4669716] (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI; Vol. 1). https://doi.org/10.1109/ICTAI.2008.142