Abstract
Feature weighting is an important task in data analyze, clustering and classification. Traditional algorithms focus on a common weight vector on the whole dataset which can easily lead to sensitiveness to the distribution of data. In contrast, a novel feature weighting algorithm called local feature weighting (LFW) that assign each sample a unique weight vector is proposed in this paper. We use clustering assumption to construct optimization task. Instead of considering the total intra-class and between-class features, we focus on the clustering performance on each training sample and the optimization goals are to minimize the total distances of a training sample to others in the same class and maximize the total distances in different classes. Data weight is added to the target function to emphasis nearby samples and finally use an iterative process to solve our problem. Experiments show that the new algorithm has a good performance on data classification. In addition, we provide a simple version of LFW which has less running time but with little accuracy loss.
The paper starting on page 293 of this volume has been retracted because a significant portion of the work was copied from the paper “Local Feature Selection for Data Classification” by Narges Armanfard, James P. Reilly, and Majid Komeili, published in 2016 in the IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 38. The erratum to this chapter is available at 10.1007/978-3-662-54395-5_26
This work was co-supported by Natural science fund project in Xinjiang (2014211A046) and the project of National Science and technology (grant no: 2014BAH13F02), and National Science and Technology Support program: Research and demonstration of virtual exhibition system for the spread of the special culture. (Project No. 2015BAK04B05).
An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-662-54395-5_26
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Jia, G., Zhao, H., Pan, Z., Wang, L. (2017). RETRACTED CHAPTER: Local Feature Weighting for Data Classification. In: Pan, Z., Cheok, A., Müller, W., Zhang, M. (eds) Transactions on Edutainment XIII. Lecture Notes in Computer Science(), vol 10092. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54395-5_25
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DOI: https://doi.org/10.1007/978-3-662-54395-5_25
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