Genetic Algorithm Based Attribute Value Taxonomy Generation for Learning Classifiers with Missing Data


The KIPS Transactions:PartB , Vol. 13, No. 2, pp. 133-138, Apr. 2006
10.3745/KIPSTB.2006.13.2.133,   PDF Download:

Abstract

Learning with Attribute Value Taxonomies (AVT) has shown that it is possible to construct accurate, compact and robust classifiers from a partially missing dataset (dataset that contains attribute values specified with different level of precision). Yet, in many cases AVTs are generated from experts or people with specialized knowledge in their domain. Unfortunately these user-provided AVTs can be time-consuming to construct and misguided during the AVT building process. Moreover experts are occasionally unavailable to provide an AVT for a particular domain. Against these backgrounds, this paper introduces an AVT generating method called GA-AVT-Learner, which finds a near optimal AVT with a given training dataset using a genetic algorithm. This paper conducted experiments generating AVTs through GA-AVT-Learner with a variety of real world datasets. We compared these AVTs with other types of AVTs such as HAC-AVTs and user-provided AVTs. Through the experiments we have proved that GA-AVT-Learner provides AVTs that yield more accurate and compact classifiers and improve performance in learning missing data.


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Cite this article
[IEEE Style]
J. U. Joo and J. H. Yang, "Genetic Algorithm Based Attribute Value Taxonomy Generation for Learning Classifiers with Missing Data," The KIPS Transactions:PartB , vol. 13, no. 2, pp. 133-138, 2006. DOI: 10.3745/KIPSTB.2006.13.2.133.

[ACM Style]
Jin U Joo and Ji Hoon Yang. 2006. Genetic Algorithm Based Attribute Value Taxonomy Generation for Learning Classifiers with Missing Data. The KIPS Transactions:PartB , 13, 2, (2006), 133-138. DOI: 10.3745/KIPSTB.2006.13.2.133.