archives

Ontology-Driven Induction of Decision Trees at Multiple Levels of Abstraction


Home 

About 

Browse 

Search 

Register 

Subscriptions 

Deposit Papers 

Help
    

Zhang, Jun, Silvescu, Adrian and Honavar, Vasant (2002) Ontology-Driven Induction of Decision Trees at Multiple Levels of Abstraction. Technical Report ISU-CS-TR 02-13, Computer Science, Iowa State University.

Full text available as:Adobe PDF

Abstract

Ontology-Driven Induction of Decision Trees at Multiple Levels of Abstraction Ontology-Driven Decision Tree Jun Zhang, Adrian Silvescu, and Vasant Honavar} Artificial Intelligence Research Laboratory Department of Computer Science Iowa State University Ames, Iowa 50011-1040 USA

Most learning algorithms for data-driven induction of pattern classifiers (e.g., the decision tree algorithm), typically represent input patterns at a single level of abstraction -- usually in the form of an ordered tuple of attribute values. However, in many applications of inductive learning -- e.g., scientific discovery, users often need to explore a data set at multiple levels of abstraction, and from different points of view. Each point of view corresponds to a set of ontological (and representational) commitments regarding the domain of interest. The choice of an ontology induces a set of representatios of the data and a set of transformations of the hypothesis space. This paper formalizes the problem of inductive learning using ontologies and data; describes an ontology-driven decision tree learning algorithm to learn classification rules at multiple levels of abstraction; and presents preliminary results to demonstrate the feasibility of the proposed approach.

Keywords:ontology, decision tree, abstraction, ontology-based learning
Comments:Zhang, J., Silvescu, A., and Honavar, V. (2002). Ontology-Driven Induction of Decision Trees at Multiple Levels of Abstraction. In: Proceedings of Symposium on Abstraction, Reformulation, and Approximation. Lecture Notes in Artificial Intellingence Volume 2371, Issue , pp 0316- Berlin: Springer-Verlag.
Subjects:Computing Methodologies: ARTIFICIAL INTELLIGENCE: General
Computing Methodologies: ARTIFICIAL INTELLIGENCE: Knowledge Representation Formalisms and Methods (F.4.1)
Computing Methodologies: ARTIFICIAL INTELLIGENCE: Learning (K.3.2)
Computing Methodologies: PATTERN RECOGNITION: General
Computing Methodologies: PATTERN RECOGNITION: Models
Computing Methodologies: PATTERN RECOGNITION: Design Methodology
Computing Methodologies: PATTERN RECOGNITION: Applications
Computing Methodologies: PATTERN RECOGNITION: Implementation (C.3)
ID code:00000291
Deposited by:Vasant Honavar on 07 December 2002



Contact site administrator at: ssg@cs.iastate.edu