(Enter summary)
Abstract: The recent emergence of data mining as a
major application of machine learning has led
to increased interest in fast rule induction algorithms.
These are able to efficiently process
large numbers of examples, under the constraint
of still achieving good accuracy. If e
is the number of examples, many rule learners
have O(e
4
) asymptotic time complexity in
noisy domains, and C4.5RULES has been empirically
observed to sometimes require O(e
3
).
Recent advances have brought this bound down
to ... (Update)
Context of citations to this paper: More
...Results are reported that show some better classification accuracy than those induced by the sequential covering approach. CWS (Domingos 1996) dynamically interleaves rule induction and performance evaluation of a current rule set. Thus a new rule (which can also be a...
...the input dataset in Noah, all rules are always induced from the entire training set. This alleviates the small disjunct problem [9, 7]. 4.1 Terms Reordering To optimize rule lookup, all terms from the training set are ordered according to their support. Thus, a new...
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BibTeX entry: (Update)
Domingos, P. (1996b). Linear time rule induction. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Menlo Park, CA, pp. 96--101. AAAI Press. http://citeseer.ist.psu.edu/15238.html More
@inproceedings{ domingos96lineartime,
author = "Pedro Domingos",
title = "Linear-Time Rule Induction",
booktitle = "Knowledge Discovery and Data Mining",
pages = "96-101",
year = "1996",
url = "citeseer.ist.psu.edu/15238.html" }
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The graph only includes citing articles where the year of publication is known.
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