Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network
Abstract
Learning general rules is a basic goal of many concept learning systems. In a 1989 paper, Holte, Acker, and Porter pointed out that this bias toward generality had resulted in a problem with small disjuncts. The problem they discussed was that small disjuncts had high rates of misclassification, and that it was difficult to eliminate the error-prone small disjuncts without affecting the performance of other disjuncts. We describe a real domain based on NYNEX MAX, an expert system that diagnoses the local loop in a telephone network. We demonstrate with two inductive learning systems that a range of disjunct sizes is important for this domain despite the relatively high error rates of the small disjuncts. We conclude that the need for smaller disjuncts is a major reason that it is difficult to learn from errorful data in this domain.
Cite
Text
Danyluk and Provost. "Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network." International Conference on Machine Learning, 1993. doi:10.1016/B978-1-55860-307-3.50017-4Markdown
[Danyluk and Provost. "Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network." International Conference on Machine Learning, 1993.](https://mlanthology.org/icml/1993/danyluk1993icml-small/) doi:10.1016/B978-1-55860-307-3.50017-4BibTeX
@inproceedings{danyluk1993icml-small,
title = {{Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network}},
author = {Danyluk, Andrea Pohoreckyj and Provost, Foster J.},
booktitle = {International Conference on Machine Learning},
year = {1993},
pages = {81-88},
doi = {10.1016/B978-1-55860-307-3.50017-4},
url = {https://mlanthology.org/icml/1993/danyluk1993icml-small/}
}