An investigation of the performance of informative samples preservation methods

Xiong, J; Li, Y

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.

Citation

Xiong, J., & Li, Y. (2012). An investigation of the performance of informative samples preservation methods. In Z. Qian, L. Cao, W. Su, T. Wang, & H. Yang (Eds.), Recent Advances in Computer Science and Information Engineering (13-18). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-25781-0_3

• Publication Date: Jan 1, 2012
• Deposit Date: Jul 8, 2015
• Pages: 13-18
• Series Title: Lecture Notes in Electrical Engineering
• Series Number: 124
• Book Title: Recent Advances in Computer Science and Information Engineering
• ISBN: 9783642257803
• DOI: https://doi.org/10.1007/978-3-642-25781-0_3
• Publisher URL: http://dx.doi.org/10.1007/978-3-642-25781-0_3
• Related Public URLs: http://link.springer.com/book/10.1007/978-3-642-25781-0