An investigation of the performance of informative samples preservation methods

Xiong, J and Li, Y 2012, 'An investigation of the performance of informative samples preservation methods' , in: Recent Advances in Computer Science and Information Engineering , Lecture Notes in Electrical Engineering, 1 (124) , Springer Berlin Heidelberg, pp. 13-18.

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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
Editors: Qian, Z, Cao, L, Su, W, Wang, T and Yang, H
Schools: Schools > School of Computing, Science and Engineering
Publisher: Springer Berlin Heidelberg
Refereed: Yes
Series Name: Lecture Notes in Electrical Engineering
ISBN: 9783642257803
Related URLs:
Funders: Non funded research
Depositing User: Yuhua Li
Date Deposited: 08 Jul 2015 12:20
Last Modified: 06 Sep 2021 07:41

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