Document (#24551)

Author

Kolluri, V.
Metzler, D.P.

Title

Knowledge guided rule learning

Source

Knowledge: creation, organization and use. Proceedings of the 62nd Annual Meeting of the American Society for Information Science, 31.10.-4.11.1999. Ed.: L. Woods

Imprint

Medford, NJ : Information Today

Year

1999

Pages

S.146-160

Series

Proceedings of the American Society for Information Science; vol.36

Abstract

Rule learning algorithms, developed by traditional supervised machine learning research community, are being used as data analysis tools for generating accurate concept definitions, given a set of instances (pre-classified) and a goal-task (concept class). Most rule learners use straightforward data driven approaches using information theoretic principles to search for statistically defined "interesting" patterns in the data sets. There are two main drawbacks with such purely data driven approaches. First, they perform poorly when insufficient data is available. Second, when large training data sets are available they tend to generate many uninteresting patterns from data sets, and usually it is left to the domain expert to distinguish the "useful" pieces of information from the rest. The size of this problem (a data mining issue onto itself) suggests the need to guide the learning system's search to relevant sub spaces within the space of all possible hypotheses. This paper explores the utility of using prior domain knowledge (in the form of taxonomies over attributes, attribute values and concept classes) to constrain the rule learner's search by requiring it to be consistent with what is already known about the domain. Spreading Activation Learning (SAL) using marker propagation techniques introduced by Aronis and Provost (1994) is used to efficiently learn over taxonomically structured attributes and attribute values. An extension to the SAL methodology to handle rule learning over concept class values is presented. By representing the range of numeric (continuous) values for attributes in the form of simplified IS A taxonomies, the SAL methodology is shown to be capable of handling numeric (continuous) attribute values. Large taxonomies over value sets (especially taxonomies over numeric value sets) usually result in too many redundant rules. This problem can be addressed by pruning the rule set using "rule interest" measures. The focus of this study is to explore the utility of taxonomic structures in rule learning and, in particular the use of taxonomic structures as a way of incorporating background knowledge in the rule learning process. Initial results obtained from an ongoing research work are presented

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5. Hassanien, A.-E.: Rough set approach for attribute reduction and rule generation : a case of patients with suspected breast cancer (2004) 0.17

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