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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Proceedings ArticleDOI
Different approaches to community evolution prediction in blogosphere
Bogdan Gliwa,Piotr Bródka,Anna Zygmunt,Stanisław Saganowski,Przemysław Kazienko,Jaroslaw Kozlak +5 more
TL;DR: Comparison to previous studies shows that using many measures to describe the group profile, and in consequence as a classifier input, can improve predictions.
Book ChapterDOI
Bootstrapping Case Base Development with Annotated Case Summaries
TL;DR: Experimental results which indicate the usefulness of learning from sentences and adding a thesaurus are presented, and the chances and limitations of leveraging the learned classifiers for full-text documents are considered.
Journal ArticleDOI
Discovering spammer communities in twitter
TL;DR: An unsupervised approach called SpamCom is proposed for detecting spammer communities in Twitter and exploits the existence of overlapping community-based features of users represented in the form of Hypergraphs to identify spammers based on their structural behavior and URL characteristics.
Journal ArticleDOI
Classification by feature partitioning
H. Altay Güvenir,Izzet Sirin +1 more
TL;DR: A new form of exemplar-based learning, based on a representation scheme called feature partitioning, and a particular implementation of this technique called CFP (for Classification by Feature Partitioning), are presented.
Journal ArticleDOI
Best-case results for nearest-neighbor learning
TL;DR: A theoretical model is proposed, in which the teacher knows the classification algorithm and chooses examples in the best way possible, using the nearest-neighbor learning algorithm, and upper and lower bounds on sample complexity for several different concept classes are developed.
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
Book
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.