Abstract
Refinement tools seek to correct faulty knowledge based systems (KBSs) by identifying and repairing faults that are indicated by training examples for which the KBS gives an incorrect solution. Refinement tools typically use a hill-climbing search to identify suitable repairs. Backtracking search algorithms, developed for constraint satisfaction problems, have been incorporated with an iterative knowledge refinement tool, to solve local maxima problems. This paper investigates how the efficiency of such a tool can be improved and introduces new and general heuristics for ordering training examples. Experimental results reveal that these heuristics applied to static and dynamic ordering of training examples can significantly improve the efficiency of the iterative refinement tool, without increasing the error-rate of the final refined KBS.
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Wiratunga, N., Craw, S. (2000). Sequencing Training Examples for Iterative Knowledge Refinement. In: Bramer, M., Macintosh, A., Coenen, F. (eds) Research and Development in Intelligent Systems XVI. Springer, London. https://doi.org/10.1007/978-1-4471-0745-3_3
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DOI: https://doi.org/10.1007/978-1-4471-0745-3_3
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