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Open AccessJournal ArticleDOI

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.

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Citations
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Journal ArticleDOI

Unifying instance-based and rule-based induction

TL;DR: In an extensive empirical study, RISE consistently achieves higher accuracies than state-of-the-art representatives of both its parent approaches, as well as a decision tree learner (C4.5).
Journal ArticleDOI

Attribute selection with fuzzy decision reducts

TL;DR: This paper introduces the concept of fuzzy decision reducts, dependent on an increasing attribute subset measure, and presents a generalization of the classical rough set framework for data-based attribute selection and reduction using fuzzy tolerance relations.
Journal ArticleDOI

Dissimilarity representations allow for building good classifiers

TL;DR: It is shown that a normal density-based classifier, based on a weighted combination of dissimilarities, can significantly improve the nearest neighbor rule with respect to the recognition accuracy and computational effort.
Proceedings Article

Applying Many-to-Many Alignments and Hidden Markov Models to Letter-to-Phoneme Conversion

TL;DR: This work presents a novel technique of training with many-to-many alignments of letters and phonemes, and applies an HMM method in conjunction with a local classification model to predict a global phoneme sequence given a word.
Journal ArticleDOI

Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals

TL;DR: A new classification algorithm, called VFI5 (for Voting Feature Intervals), is developed and applied to problem of differential diagnosis of erythemato-squamous diseases, where the domain contains records of patients with known diagnosis and the classifier learns how to differentiate a new case in the domain.
References
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Journal ArticleDOI

Induction of Decision Trees

J. R. Quinlan
- 25 Mar 1986 - 
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.
Book

Classification and regression trees

Leo Breiman
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

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.