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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Patent
Methods, systems, and media for detecting covert malware
Brian M. Bowen,Pratap Prabhu,Vasileios P. Kemerlis,Stylianos Sidiroglou,Salvatore J. Stolfo,Angelos D. Keromytis +5 more
TL;DR: In this article, a method for detecting covert malware in a computing environment is provided, the method comprising: generating simulated user activity outside of the computing environment; conveying the simulated user activities to an application inside the environment; and determining whether a decoy corresponding to the simulated users' activity has been accessed by an unauthorized entity.
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Personal recognition using hand shape and texture
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TL;DR: The experimental results demonstrate that while majority of palmprint or hand-shape features are useful in predicting the subjects identity, only a small subset of these features are necessary in practice for building an accurate model for identification.
Proceedings ArticleDOI
Handling concept drifts in incremental learning with support vector machines
TL;DR: Empirical results using benchmark machine learning datasets are provided to show that support vectors form a svccdnct and suficient set for block-by-block incremental learning.
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A feature selection model based on genetic rank aggregation for text sentiment classification
Aytuğ Onan,Serdar Korukoglu +1 more
TL;DR: An ensemble approach for feature selection is presented, which aggregates the several individual feature lists obtained by the different feature selection methods so that a more robust and efficient feature subset can be obtained.
Proceedings Article
MBT: A Memory-Based Part of Speech Tagger-Generator
TL;DR: A large-scale application of the memory-based approach to part of speech tagging is shown to be feasible, obtaining a tagging accuracy that is on a par with that of known statistical approaches, and with attractive space and time complexity properties when using IGTree, a tree-based formalism for indexing and searching huge case bases.
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.