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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Automatic recommendation of classification algorithms based on data set characteristics
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Book ChapterDOI
SVM: Support Vector Machines
Hui Xue,Qiang Yang,Songcan Chen +2 more
TL;DR: Support vector machines (SVM) as discussed by the authors are among the most robust and accurate methods in all well-known data mining algorithms and have a sound theoretical foundation rooted in statistical learning theory, require only as few as a dozen examples for training, and are insensitive to the number of dimensions.
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A physical process and machine learning combined hydrological model for daily streamflow simulations of large watersheds with limited observation data
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Journal ArticleDOI
Experimental study on prototype optimisation algorithms for prototype-based classification in vector spaces
Maria Teresa Lozano,José Martínez Sotoca,José Salvador Sánchez,Filiberto Pla,E. Pkalska,Robert P. W. Duin +5 more
TL;DR: In this paper, an experimental study of some old and new prototype optimization techniques is presented, in which the prototypes are either selected or generated from the given data, and evaluated on real data, represented in vector spaces, by comparing their resulting reduction rates and classification performance.
Journal ArticleDOI
A class boundary preserving algorithm for data condensation
TL;DR: A new approach is introduced, the Class Boundary Preserving Algorithm (CBP), which is a multi-stage method for pruning the training set, based on a simple but very effective heuristic for instance removal.
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.