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An Investigation of the Performance of Informative Samples Preservation Methods

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Xiong, Jianlin and Li, Yuhua (2012) An Investigation of the Performance of Informative Samples Preservation Methods. In: Lecture Notes in Electrical Engineering: Recent Advances in Computer Science and Information Engineering. Springer, pp. 13-18. ISBN na [Book section]

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DOI: 10.1007/978-3-642-25781-0_3

Abstract

Instance-based learning algorithms make prediction/generalization based on the stored instances. Storing all instances of large data size applications causes huge memory requirements and slows program execution speed; it may make the prediction process impractical or even impossible. Therefore researchers have made great efforts to reduce the data size of instance-based learning algorithms by selecting informative samples. This paper has two main purposes. First, it investigates recent developments in informative sample preservation methods and identifies five representative methods for use in this study. Second, the five selected methods are implemented in a standardized input-output interface so that the programs can be used by other researchers, their performance in terms of accuracy and reduction rates are compared on ten benchmark classification problems. K-nearest neighbor is employed as the classifier in the performance comparison.

Item Type:Book section
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:21102
Deposited By:Dr Yuhua Li
Deposited On:20 Feb 2012 14:10
Last Modified:20 Feb 2012 14:10

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