@inproceedings{c90335d414e94588bed49a6dc175195e,
title = "Feature weighting by RELIEF based on local hyperplane approximation",
abstract = "In this paper, we propose a new feature weighting algorithm through the classical RELIEF framework. The key idea is to estimate the feature weights through local approximation rather than global measurement, as used in previous methods. The weights obtained by our method are more robust to degradation of noisy features, even when the number of dimensions is huge. To demonstrate the performance of our method, we conduct experiments on classification by combining hyperplane KNN model (HKNN) and the proposed feature weight scheme. Empirical study on both synthetic and real-world data sets demonstrate the superior performance of the feature selection for supervised learning, and the effectiveness of our algorithm.",
keywords = "Classification, Feature weighting, KNN, local hyperplane, RELIEF",
author = "Hongmin Cai and NG, {Kwok Po}",
note = "Copyright: Copyright 2012 Elsevier B.V., All rights reserved.; 16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012 ; Conference date: 29-05-2012 Through 01-06-2012",
year = "2012",
doi = "10.1007/978-3-642-30220-6_28",
language = "English",
isbn = "9783642302190",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 2",
pages = "335--346",
booktitle = "Advances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings",
edition = "PART 2",
}