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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Journal Article
Learning in Environments with Unknown Dynamics: Towards more Robust Concept Learners
TL;DR: An incremental decision tree that is updated with incoming examples and is better than evaluated methods in its ability to deal with concept drift when dealing with problems in which: concept change occurs at different speeds, noise may be present and, examples may arrive from different areas of the problem domain.
Quantifying the Impact of Learning Algorithm Parameter Tuning (short version)
Niklas Lavesson,Paul Davidsson +1 more
TL;DR: In this paper, the impact of learning algorithm optimization by means of parameter tuning is studied, where two quality attributes, sensitivity and classification performance, are investigated, and two metr...
Proceedings ArticleDOI
Memory-Based Learning: Using Similarity for Smoothing
Jakub Zavrel,Walter Daelemans +1 more
TL;DR: It is argued that feature weighting methods in the Memory-Based paradigm can offer the advantage of automatically specifying a suitable domain-specific hierarchy between most specific and most general conditioning information without the need for a large number of parameters.
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Accurate prediction of hot spot residues through physicochemical characteristics of amino acid sequences.
TL;DR: This article investigated the problem of identifying hot spots using only physicochemical characteristics extracted from amino acid sequences and significantly outperformed other machine learning algorithms and state‐of‐the‐art hot spot predictors.
Journal ArticleDOI
Image-Based Delineation and Classification of Built Heritage Masonry
TL;DR: The ground work carried out to make this tool possible is presented: the automatic, image-based delineation of stone masonry as the input to a classifier for a geometrically characterized feature of a built heritage object.
References
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Journal ArticleDOI
Classification and Regression Trees.
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
TL;DR: The methodology used to construct tree structured rules is the focus of a monograph as mentioned in this paper, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
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.