Attribute value taxonomy generation through matrix based adaptive genetic algorithm

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

doi:10.1109/ICTAI.2008.142

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 proceeding › Conference contribution

9 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 language	English (US)
Title of host publication	Proceedings - 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08
Pages	393-400
Number of pages	8
DOIs	https://doi.org/10.1109/ICTAI.2008.142
State	Published - Dec 22 2008
Event	20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08 - Dayton, OH, United States Duration: Nov 3 2008 → Nov 5 2008

Publication series

Name	Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume	1
ISSN (Print)	1082-3409

Other

Other	20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08
Country/Territory	United States
City	Dayton, OH
Period	11/3/08 → 11/5/08

All Science Journal Classification (ASJC) codes

Software
Artificial Intelligence
Computer Science Applications

Access to Document

10.1109/ICTAI.2008.142

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). Article 4669716 (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI; Vol. 1). https://doi.org/10.1109/ICTAI.2008.142

@inproceedings{3f50c081b6784359856302fac0a1bd89,

title = "Attribute value taxonomy generation through matrix based adaptive genetic algorithm",

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.",

author = "Hyunsung Jo and Na, {Yong Chan} and Byonghwa Oh and Jihoon Yang and Vasant Honavar",

year = "2008",

month = dec,

day = "22",

doi = "10.1109/ICTAI.2008.142",

language = "English (US)",

isbn = "9780769534404",

series = "Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI",

pages = "393--400",

booktitle = "Proceedings - 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08",

note = "20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08 ; Conference date: 03-11-2008 Through 05-11-2008",

}

Jo, H, Na, YC, 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., 4669716, Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, vol. 1, pp. 393-400, 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08, Dayton, OH, United States, 11/3/08. https://doi.org/10.1109/ICTAI.2008.142

Attribute value taxonomy generation through matrix based adaptive genetic algorithm. / Jo, Hyunsung; Na, Yong Chan; Oh, Byonghwa et al.
Proceedings - 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08. 2008. p. 393-400 4669716 (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Attribute value taxonomy generation through matrix based adaptive genetic algorithm

AU - Jo, Hyunsung

AU - Na, Yong Chan

AU - Oh, Byonghwa

AU - Yang, Jihoon

AU - Honavar, Vasant

PY - 2008/12/22

Y1 - 2008/12/22

N2 - 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.

AB - 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.

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Attribute value taxonomy generation through matrix based adaptive genetic algorithm

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