• Journal of the Korean Society of Marine Environment & Safety
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• v.13 no.3
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• pp.207-212
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• 2007

This paper aims to measure and evaluate the technical efficiency with two inputs and four outputs with the use of fuzzy DEA in Korean RCC/RSC. Especially, this paper included not only the marine accident data which occurred for the analysis in particular but also the possibility data of a potential marine accident by an Environmental Stress value and analyzed the technical efficiency. And in this paper, asymmetrical triangular fuzzy number is presented about inputs/ outputs data and a procedure is suggested for it's solution. The basic idea is to transform the fuzzy CCR model into a crisp linear programming problem by applying an alternative ${\alpha}$-cut approach. Also this paper propose a ranking method for fuzzy RCC/RSC using presented fuzzy DEA approach. The result, when ${\alpha}$-cut is 0.5, efficiency priority is found in the order of YS, BS, MP, TY, JJ, PH, US, IC, SC, DH, GS, TA, WD RCC/RSC. Finally, Inefficiency TA, WD RCC/RSC have to benchmarking with reference sets.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.599-604
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• 2005

This paper presents fuzzy rules to predict diagnosis of Wisconsin breast cancer with minimized number of feature in put using the neural network with weighted fuzzy membership functions (NEWFM) and the non-overlap area distribution measurement method. NEWFM is capable of self-adapting weighted membership functions from the given the Wisconsin breast cancer clinical training data. n set of small, medium, and large weighted triangular membership functions in a hyperbox are used for representing n set of featured input. The membership functions are randomly distributed and weighted initially, and then their positions and weights are adjusted during learning. After learning, prediction rules are extracted directly from n set of enhanced bounded sums of n set of small, medium, and large weighted fuzzy membership functions. Then, the non-overlap area distribution measurement method is applied to select important features by deleting less important features. Two sets of prediction rules extracted from NEWFM using the selected 4 input features out of 9 features outperform to the current published results in number of set of rules, number of input features, and accuracy with 99.71%.