Thumbnail
Access Restriction
Subscribed

Author Vluymans, S. ♦ Saeys, Y. ♦ Cornelis, C. ♦ Teredesai, A. ♦ De Cock, M.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2015
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Training ♦ Prototypes ♦ Set theory ♦ Electronic mail ♦ Prediction algorithms ♦ Additives ♦ Machine learning algorithms
Abstract Instance selection methods are a class of preprocessing techniques that have been widely studied in machine learning to remove redundant or noisy instances from a training set. The main focus of such prior efforts has been on the selection of suitable training instances to perform a classification task for crisp class labels. In this paper, we propose a novel instance selection technique termed Fuzzy Rough Set Prototype Selection for Regression (FRPS-R) for solving regression problems, where the outcome is continuous. We use concepts from fuzzy rough set theory and extend the currently well-known fuzzy rough set prototype selection technique to model the quality of all available elements and then use a wrapper approach to select an optimal subset of high-quality instances; thereby generalizing the idea. Our experimental evaluation shows that the application of our proposed instance selection technique can significantly improve the predictive performance of the weighted k-nearest neighbor regression algorithm, in particular when noise is present in the original training set.
Description Author affiliation: VIB Inflammation Res. Center, Zwijnaarde, Belgium (Saeys, Y.) || Dept. of Appl. Math., Comput. Sci. & Stat., Ghent Univ., Ghent, Belgium (Vluymans, S.; Cornelis, C.) || Center for Data Sci., Univ. of Washington Tacoma, Tacoma, WA, USA (Teredesai, A.; De Cock, M.)
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2015-08-02
Publisher Place Turkey
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781467374286
Size (in Bytes) 230.29 kB
Page Count 8
Starting Page 1
Ending Page 8


Source: IEEE Xplore Digital Library