Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/57148
Title: A novel learning vector quantization inference classifier
Authors: Chakkraphop Maisen
Sansanee Auephanwiriyakul
Nipon Theera-Umpon
Keywords: Computer Science
Mathematics
Issue Date: 1-Jan-2017
Abstract: © Springer International Publishing AG 2017. One of the popular tools in pattern recognition is a neuro-fuzzy system. Most of the neuro-fuzzy systems are based on a multi-layer perceptrons. In this paper, we incorporate learning vector quantization in a neuro-fuzzy system. The prototype update equation is based on the learning vector quantization while the gradient descent technique is used in the weight update equation. Since weights contain informative information, they are exploited to select a good feature set. There are 8 data sets used in the experiment, i.e., Iris Plants, Wisconsin Breast Cancer (WBC), Pima Indians Diabetes, Wine, Ionosphere, Colon Tumor, Diffuse Large B-Cell Lymphoma (DLBCL), and Glioma Tumor (GLI_85). The results show that our algorithm provides good classification rates on all data sets. It is able to select a good feature set with a small number of features. We compare our results indirectly with the existing algorithms as well. The comparison result shows that our algorithm performs better than those existing ones.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85018582024&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/57148
ISSN: 16113349
03029743
Appears in Collections:CMUL: Journal Articles

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