TY - GEN
T1 - Ontology-driven induction of decision trees at multiple levels of abstraction
AU - Zhang, Jun
AU - Silvescu, Adrian
AU - Honavar, Vasant
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2002.
PY - 2002
Y1 - 2002
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=62649104314&partnerID=8YFLogxK
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U2 - 10.1007/3-540-45622-8_25
DO - 10.1007/3-540-45622-8_25
M3 - Conference contribution
AN - SCOPUS:62649104314
SN - 3540439412
SN - 9783540439417
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 316
EP - 323
BT - Abstraction, Reformulation, and Approximation - 5th International Symposium, SARA 2002, Proceedings
A2 - Koenig, Sven
A2 - Holte, Robert C.
PB - Springer Verlag
T2 - 5th International Symposium on Abstraction, Reformulation, and Approximation, SARA 2002
Y2 - 2 August 2002 through 4 August 2002
ER -