• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.1 s.307
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• pp.39-44
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• 2006

In this paper, search result list synthesis method is proposed using borda count method for still image retrieval based on MPEG-7 descriptors. MPEG-7 standardizes descriptors that extract feature information from media data. In many cases, using a single descriptor lacks of correctness, it is suggested to use multiple descriptors to enhance retrieval efficiency. In this paper, retrieval efficiency enhancement is achieved by combining multiple search results which are from each descriptor. In combining search result, newly calculated borda count method is proposed. Comparing current frequency compensated calculation, rank considered frequency compensation is used to score animage in database. This combining method is considered in Content based image retrieval system with relevance feedback algorithm which uses high level information from system user. In each relevance iteration step, adoptive borda count method is used to calculate score of images.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.593-596
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• 1999

In this paper, we propose an image retrieval system using the MBCM(Modified Borda Count method) in CME(Combining Multiple Experts). It combines color-, shape- and texture-based retrieval sub-systems. CME method can complementarily combine results of each retrieval system, which uses different features. There are some problems when the Borda count method in pattern recognition is applied to image retrieval. Thus, we propose a modified Borda count method to solve these problems. In the experiment, our method reduces false positive errors and produces better results than that of each retrieval module that uses only one feature.

• Journal of Korea Water Resources Association
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• v.46 no.2
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• pp.99-109
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• 2013

Increasing complexity of the basin environments makes it difficult for single decision maker to consider all relevant aspects of problem, and thus the uncertainty of decision making grows. This study attempts to develop an approach to quantify the spatial flood vulnerability of South Korea. Fuzzy TOPSIS is used to calculate individual preference by each group and then three GDM techniques (Borda count method, Condorcet method, and Copeland method) are used to integrate the individual preference. Finally, rankings from Fuzzy TOPSIS, TOPSIS, and GDM are compared with Spearman rank correlation, Kendall rank correlation, and Emond & Mason rank correlation. As a result, the rankings of some areas are dramatically changed by the use of GDM techniques. Because GDM technique in regional vulnerability assessment may cause a significant change in priorities, the model presented in this study should be considered for objective flood vulnerability assessment.

• Journal of KIISE:Software and Applications
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• v.29 no.3
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• pp.192-201
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• 2002

Automated text classification is considered as an important method to manage and process a huge amount of documents in digital forms that are widespread and continuously increasing. Recently, text classification has been addressed with machine learning technologies such as k-nearest neighbor, decision tree, support vector machine and neural networks. However, only few investigations in text classification are studied on real problems but on well-organized text corpus, and do not show their usefulness. This paper proposes and analyzes text classification methods for a real application, email document classification task. First, we propose a combining method of multiple neural networks that improves the performance through the combinations with maximum and neural networks. Second, we present another strategy of combining multiple machine learning classifiers. Voting, Borda count and neural networks improve the overall classification performance. Experimental results show the usefulness of the proposed methods for a real application domain, yielding more than 90% precision rates.