Journal
KNOWLEDGE-BASED SYSTEMS
Volume 173, Issue -, Pages 150-162Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.knosys.2019.02.034
Keywords
Incomplete data; Missing values; SVM; Kernel methods; RBF
Categories
Funding
- National Science Centre (Poland) [2016/21/D/ST6/00980, 2015/19/B/ST6/01819]
- European Union from the European Regional Development Fund under the Smart Growth Operational Programme 2014-2020.
Ask authors/readers for more resources
We construct genRBF kernel, which generalizes standard Gaussian RBF kernel to the case of incomplete data. Instead of using typical imputation techniques, which fill missing attributes by single values, we model possible outcomes at missing coordinates using data distribution. This allows to derive analytical formula for the expected value of RBF kernel taken over all possible imputations, which is a basic idea behind our method. In particular, for complete observations genRBF reduces to standard RBF kernel. Experiments show that introduced kernel applied to SVM classifier and regressor gives better results than state-of-the-art methods, especially in the case when large number of features is missing. Moreover, genRBF is easy to implement and can be used together with any kernel approach without any additional modifications. (C) 2019 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available