C4.5: programs for machine learning
C4.5: programs for machine learning
Multiple Comparisons in Induction Algorithms
Machine Learning
NYNEX MAX: A Telephone Trouble Screening Expert
IAAI '91 Proceedings of the The Third Conference on Innovative Applications of Artificial Intelligence
Tree Induction for Probability-Based Ranking
Machine Learning
Tree induction vs. logistic regression: a learning-curve analysis
The Journal of Machine Learning Research
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The Scrubber 3 system monitors problems in the local loop of the telephone network, making automated decisions on tens of millions of cases a year, many of which lead to automated actions. Scrubber saves Bell Atlantic millions of dollars annually, by reducing the number of inappropriate technician dispatches. Scrubber's core knowledge base, the trouble isolation module (TIM), is a probability estimation tree constructed via several data mining processes. TIM currently is deployed in the Delphi system, which serves knowledge to multiple applications. As compared to previous approaches, TIM is more general, more robust, and easier to update when the network or user requirements change. Under certain circumstances it also provides better classifications. In fact, TIM's knowledge is general enough that it now serves a second deployed application. One of the most interesting aspects of the construction of TIM is that data mining was used not only in the traditional sense, namely, building a model from a warehouse of actual historical cases, but it was also used to produce an understandable model of the knowledge contained in an earlier, successful diagnostic system, which had evolved into opacity over years of operation.