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Abstract: In this paper, we address the problem of case-based learning in the presence of irrelevant features. We review previous work on attribute selection and present a new algorithm, Oblivion, that carries out greedy pruning of oblivious decision trees, which effectively store a set of abstract cases in memory. We hypothesize that this approach will efficiently identify relevant features even when they interact, as in parity concepts. We report experimental results on artificial domains that support... (Update)
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BibTeX entry: (Update)
Langley, P., and Sage, S. 1994. Oblivious decision trees and abstract cases. In Working Notes of the AAAI94 Workshop on Case-Based Reasoning. In press. http://citeseer.ist.psu.edu/langley94oblivious.html More
@inproceedings{ langley94oblivious,
author = "Pat Langley and Stephanie Sage",
title = "Oblivious Decision Trees and Abstract Cases",
booktitle = "Working Notes of the {AAAI}-94 Workshop on Case-Based Reasoning",
publisher = "AAAI Press",
year = "1994",
url = "citeseer.ist.psu.edu/langley94oblivious.html" }
Citations (may not include all citations):125 Learning with many irrelevant features - Almuallim, Dietterich - 1991
65 A study of instance-based algorithms for supervised learning.. (context) - Aha - 1990
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