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

@incollection{citeulike:1714450, abstract = {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.}, author = {Zhang, Jun and Silvescu, Adrian and Honavar, Vasant}, citeulike-article-id = {1714450}, citeulike-linkout-0 = {http://www.springerlink.com/content/g99796jnh47fb58b}, journal = {Abstraction, Reformulation, and Approximation}, keywords = {abstraction, decision\_tree, knowledge}, pages = {316+}, posted-at = {2007-10-01 09:47:20}, priority = {0}, title = {Ontology-Driven Induction of Decision Trees at Multiple Levels of Abstraction}, url = {http://www.springerlink.com/content/g99796jnh47fb58b}, year = {2002} }

TY - CHAP ID - citeulike:1714450 L3 - citeulike-article-id:1714450 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. JF - Abstraction, Reformulation, and Approximation TI - Ontology-Driven Induction of Decision Trees at Multiple Levels of Abstraction SP - 316 KW - abstraction KW - decision_tree KW - knowledge AU - Zhang, Jun AU - Silvescu, Adrian AU - Honavar, Vasant PY - 2002/// UR - http://www.springerlink.com/content/g99796jnh47fb58b ER -