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Data-Driven Theory Refinement Using KBDistAl
Lecture Notes in Computer Science
  • Jihoon Yang, HRL Laboratories
  • Rajesh Parekh, Allstate Research & Planning Center
  • Vasant Honavar, Iowa State University
  • Drena Dobbs, Iowa State University
Document Type
Conference Proceeding
Publication Version
Accepted Manuscript
Publication Date
Conference Title
Third International Symposium, IDA-99
Conference Date
August 9–11, 1999
(52.3702157, 4.895167899999933)
Knowledge based artificial neural networks offer an attractive approach to extending or modifying incomplete knowledge bases or domain theories through a process of data-driven theory refinement. We present an efficient algorithm for data-driven knowledge discovery and theory refinement using DistAl, a novel (inter-pattern distance based polynomial time) constructive neural network learning algorithm. the initial domain theory comprising of propositional rules is translated into a knowledge based network. The domain theory is modified using DistAl which adds new neurons to the existing network as needed to reduce classification errors associated with the incomplete domain theory on labeled training examples. The proposed algorithm is capable of handling patterns represented using binary, nominal, as well as numeric (real-valued) attributes. Results of experiments on several datasets for financial advisor and the human genome project indicate that the performance of the proposed algorithm compares quite favorably with other algorithms for constructionist theory refinement (including those that require substantially more computational resources) both in terms of generalization accuracy and network size.

This is a proceeding from Lecture Notes in Computer Science 1642 (1999): 331, doi: 10.1007/3-540-48412-4_28. Posted with permission.

Copyright Owner
Springer-Verlag Berlin Heidelberg
File Format
Citation Information
Jihoon Yang, Rajesh Parekh, Vasant Honavar and Drena Dobbs. "Data-Driven Theory Refinement Using KBDistAl" Amsterdam, The NetherlandsLecture Notes in Computer Science Vol. 1642 (1999) p. 331 - 342
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