G. Webb  Home/Search
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Abstract: Optimal Pruning for Unordered Search is a search algorithm that enables complete search through the space of possible disjuncts at the inner level of a covering algorithm. This algorithm takes as inputs an evaluation function, e, a training set, t, and a set of specialisation operators, o. It outputs a set of operators from o that creates a classifier that maximises e with respect to t. While OPUS has exponential worst case time complexity, the algorithm is demonstrated to reach solutions for... (Update)
Context of citations to this paper: More
.... the success of the bias may help to explain why replacing greedy searches for rules with more exhaustive searches (e.g. Rymon [23] Webb [24]) has not been beneficial on most databases. Additional search would be useful to detect complex interactions among variables to...
...from each rule, we can directly search through each path of the tree to find new attributes. A systematic search method with pruning [Webb, 1993] using an information based evaluation function is a reasonable choice, since it can find the best new attribute from a path in...
Cited by: More
Inclusive pruning: A new class of pruning rule for unordered.. - Webb (1996) (Correct)
Rule Induction of Computer Events - Vilalta, Ma, Hellerstein (2001) (Correct)
Inclusive pruning: A new class of pruning rule for unordered.. - Webb (1996) (Correct)
Active bibliography (related documents): More All
0.3: OPUS: A systematic search algorithm and its application to.. - Webb (1993) (Correct)
0.3: Constructing New Attributes for Decision Tree Learning - Zheng (1996) (Correct)
0.2: Inclusive pruning: A new class of pruning axiom for unordered.. - Webb (Correct)
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0.2: OPUS: An Efficient Admissible Algorithm for Unordered Search - Webb (1995) (Correct)
0.2: Comparative evaluation of alternative induction engines for .. - Webb, Chiu, Kuzmycz (1997) (Correct)
0.2: Feature Based Modelling - Webb (Correct)
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8: An SE-tree based Characterization of the Induction Problem - Rymon - 1993
8: Efficiently inducing determinations: A complete and systematic search algorithm .. - Schlimmer - 1993
8: Boolean feature discovery in empirical learning (context) - Pagallo, Haussler - 1990
BibTeX entry: (Update)
Webb, G. I. Systematic search for categorical attribute-value data-driven machine learning. In Foo, N. and Rowles, C., editors, AI `93. World Scientific, Singapore, 1993. http://citeseer.ist.psu.edu/webb93systematic.html More
@misc{ webb93systematic,
author = "G. Webb",
title = "Systematic search for categorical attribute-value data-driven machine learning",
text = "Webb, G. I. Systematic search for categorical attribute-value data-driven
machine learning. In Foo, N. and Rowles, C., editors, AI `93. World Scientific,
Singapore, 1993.",
year = "1993",
url = "citeseer.ist.psu.edu/webb93systematic.html" }
Citations (may not include all citations):667 UCI Repository of machine learning databases (context) - Murphy, Aha - 1993
271 Efficient induction of logic programs - Muggleton, Feng - 1990
180 The CN2 induction algorithm (context) - Clark, Niblett - 1989
68 Rule induction with CN2: some recent improvements (context) - Clark, Boswell - 1991
55 Pattern recognition as rule-guided inductive inference (context) - Michalski - 1980
41 Determinate Literals in Inductive Logic Programming (context) - Quinlan - 1991
31 An SE-tree based characterization of the induction problem - Rymon - 1993
4 Techniques for efficient empirical induction (context) - Webb - 1990
4 Man-machine collaboration for knowledge acquisition - Webb - 1992
1 Search strategies for induction by generalization (context) - Webb - 1993
The graph only includes citing articles where the year of publication is known.
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