Identifying the dominant species of tropical wood species using histogram intersection method
This research is addresses to determine the dominant species that located in the overlapped clusters produced by the Kohonen Self-organizing Map (KSOM). Before, KSOM algorithm able to cluster the tropical wood species data set effectively and accurately according to the wood features, which is wood...
Published in: | IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society |
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2-s2.0-84973098140 Ahmad A.; Yusof R.; Mitsukura Y. Identifying the dominant species of tropical wood species using histogram intersection method 2015 IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society 10.1109/IECON.2015.7392572 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84973098140&doi=10.1109%2fIECON.2015.7392572&partnerID=40&md5=1c483c0571014b03b34cb73e0d02daee This research is addresses to determine the dominant species that located in the overlapped clusters produced by the Kohonen Self-organizing Map (KSOM). Before, KSOM algorithm able to cluster the tropical wood species data set effectively and accurately according to the wood features, which is wood pores sizes. Unfortunately, there are seven overlapped clusters in the clustering result and this is due to similarity features among the wood species. This problem has caused difficulty in determining the separation boundary amongst clusters, where the most dominant species for every overlapped cluster is difficult to identify. As for a solution, the Histogram Intersection (HI) method is proposed in this research to solve this problem. From the experiments, the HI has proved that it can determine the dominant species of the overlapped clusters effectively resulting in an improvement of 1.12% of the classification accuracy, compared with the clustering result without HI technique. It has shown the implementation of this method has successfully solved the problem occurred. © 2015 IEEE. Institute of Electrical and Electronics Engineers Inc. English Conference paper |
author |
Ahmad A.; Yusof R.; Mitsukura Y. |
spellingShingle |
Ahmad A.; Yusof R.; Mitsukura Y. Identifying the dominant species of tropical wood species using histogram intersection method |
author_facet |
Ahmad A.; Yusof R.; Mitsukura Y. |
author_sort |
Ahmad A.; Yusof R.; Mitsukura Y. |
title |
Identifying the dominant species of tropical wood species using histogram intersection method |
title_short |
Identifying the dominant species of tropical wood species using histogram intersection method |
title_full |
Identifying the dominant species of tropical wood species using histogram intersection method |
title_fullStr |
Identifying the dominant species of tropical wood species using histogram intersection method |
title_full_unstemmed |
Identifying the dominant species of tropical wood species using histogram intersection method |
title_sort |
Identifying the dominant species of tropical wood species using histogram intersection method |
publishDate |
2015 |
container_title |
IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society |
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doi_str_mv |
10.1109/IECON.2015.7392572 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84973098140&doi=10.1109%2fIECON.2015.7392572&partnerID=40&md5=1c483c0571014b03b34cb73e0d02daee |
description |
This research is addresses to determine the dominant species that located in the overlapped clusters produced by the Kohonen Self-organizing Map (KSOM). Before, KSOM algorithm able to cluster the tropical wood species data set effectively and accurately according to the wood features, which is wood pores sizes. Unfortunately, there are seven overlapped clusters in the clustering result and this is due to similarity features among the wood species. This problem has caused difficulty in determining the separation boundary amongst clusters, where the most dominant species for every overlapped cluster is difficult to identify. As for a solution, the Histogram Intersection (HI) method is proposed in this research to solve this problem. From the experiments, the HI has proved that it can determine the dominant species of the overlapped clusters effectively resulting in an improvement of 1.12% of the classification accuracy, compared with the clustering result without HI technique. It has shown the implementation of this method has successfully solved the problem occurred. © 2015 IEEE. |
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Institute of Electrical and Electronics Engineers Inc. |
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language |
English |
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Conference paper |
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scopus |
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Scopus |
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1809677608104755200 |