Instance-Based Learning Algorithms
TLDR
This paper describes how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy and extends the nearest neighbor algorithm, which has large storage requirements.Abstract:
Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to solve incremental learning tasks. In this paper, we describe a framework and methodology, called instance-based learning, that generates classification predictions using only specific instances. Instance-based learning algorithms do not maintain a set of abstractions derived from specific instances. This approach extends the nearest neighbor algorithm, which has large storage requirements. We describe how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy. While the storage-reducing algorithm performs well on several real-world databases, its performance degrades rapidly with the level of attribute noise in training instances. Therefore, we extended it with a significance test to distinguish noisy instances. This extended algorithm's performance degrades gracefully with increasing noise levels and compares favorably with a noise-tolerant decision tree algorithm.read more
Citations
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Book ChapterDOI
Addressing the Selective Superiority Problem: Automatic Algorithm/Model Class Selection
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Learning to Filter Unsolicited Commercial E-Mail
TL;DR: The architecture of a fully implemented learning-based anti-spam filter is described, and an analysis of its behavior in real use over a period of seven months is presented.
Journal ArticleDOI
Maximum Ambiguity-Based Sample Selection in Fuzzy Decision Tree Induction
TL;DR: Experimental results show that generalization ability of the tree based on the selection mechanism is far more superior to that based on random selection mechanism and the adjustment of the fuzzy decision tree is minimized when adding selected samples to the training set.
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A memetic algorithm for evolutionary prototype selection: A scaling up approach
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Journal ArticleDOI
A Data-Driven Approach for Monitoring Blade Pitch Faults in Wind Turbines
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References
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Journal ArticleDOI
Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
MonographDOI
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
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Classification and regression trees
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Journal ArticleDOI
Nearest neighbor pattern classification
Thomas M. Cover,Peter E. Hart +1 more
TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.