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Representation Design and Brute-force Induction in a Boeing Manufacturing Domain (1994)
| Venue: | Applied Artificial Intelligence |
| Citations: | 46 - 4 self |
BibTeX
@ARTICLE{Riddle94representationdesign,
author = {Patricia Riddle and Richard Segal and Oren Etzioni},
title = {Representation Design and Brute-force Induction in a Boeing Manufacturing Domain},
journal = {Applied Artificial Intelligence},
year = {1994},
volume = {8},
pages = {125--147}
}
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Abstract
We applied inductive classification techniques to data collected in a Boeing plant with the goal of uncovering possible flaws in the manufacturing process. This application led us to explore two aspects of classical decision-tree induction: (1) Preprocessing and postprocessing and (2) brute-force induction. For preprocessing and postprocessing, much of our effort was focused on the pre-processing of raw data to make it suitable for induction and the post-processing of learned rules to make them useful to factory personnel. For brute-force induction, in contrast with standard methods, which perform a greedy search of the space of decision trees, we formulated an algorithm that conducts an exhaustive, depth-bounded search for accurate predictive rules. We demonstrate the efficacy of our approach with specific examples of learned rules and by quantitative comparisons with decision-tree algorithms (C4 and CART). 3 This research was funded in part by a Boeing Computer Services contract wi...
Keyphrases
brute-force induction representation design boeing manufacturing domain learned rule boeing plant boeing computer service contract wi decision tree possible flaw factory personnel manufacturing process standard method raw data depth-bounded search accurate predictive rule specific example inductive classification technique greedy search quantitative comparison classical decision-tree induction decision-tree algorithm
