A novel learning vector quantization inference classifier
© 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 equat...
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th-cmuir.6653943832-409172017-09-28T04:14:34Z A novel learning vector quantization inference classifier Maisen C. Auephanwiriyakul S. Theera-Umpon N. © 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. 2017-09-28T04:14:33Z 2017-09-28T04:14:33Z 2017-01-01 Book Series 03029743 2-s2.0-85018582024 10.1007/978-3-319-54472-4_50 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85018582024&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/40917 |
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© 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. |
| format |
Book Series |
| author |
Maisen C. Auephanwiriyakul S. Theera-Umpon N. |
| spellingShingle |
Maisen C. Auephanwiriyakul S. Theera-Umpon N. A novel learning vector quantization inference classifier |
| author_facet |
Maisen C. Auephanwiriyakul S. Theera-Umpon N. |
| author_sort |
Maisen C. |
| title |
A novel learning vector quantization inference classifier |
| title_short |
A novel learning vector quantization inference classifier |
| title_full |
A novel learning vector quantization inference classifier |
| title_fullStr |
A novel learning vector quantization inference classifier |
| title_full_unstemmed |
A novel learning vector quantization inference classifier |
| title_sort |
novel learning vector quantization inference classifier |
| publishDate |
2017 |
| url |
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85018582024&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/40917 |
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