• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.229-236
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• 2004

This article presents a multiple comparison ranking procedure for several products of the Poisson rates. A preference probability matrix that warrants the optimal comparison ranking is introduced. Using a Bayesian Monte Carlo method, we develop simulation-based procedure to estimate the matrix and obtain the optimal ranking via a row-sum scores method. Necessary theory and two illustrative examples are provided.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.1027-1036
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• 2017

Multiple comparisons are required to confirm whether or not something is significant if the null hypothesis to test whether the difference between more than three treatments is rejected in a one-way layout. There are both parametric multiple comparison method Tukey (1953) and Nonparametric multiple comparison method based on Kruskal-Wallis (1952).This procedure is applied to a mixed sample of all data and then an average ranking is used for each of three or more treatments. In this paper, a new nonparametric multiple comparison procedure based on joint placements for a one-way layout as extension of the joint placements described in Chung and Kim (2007) was proposed. Monte Carlo simulation is also adapted to compare the family wise error rate (FWE) and the power of the proposed method with previous methods.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.61 no.2
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• pp.92-97
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• 2016

This experiment aimed to compare the sensory ability of experts and untrained to evaluate three rice varieties by using five sensory evaluation methods. All panelists showed significant differences in their sensory abilities to distinguish among Haiami, Chucheong, and Dasan 1 rice varieties when using the duo-trio test and triangle test. The expert panelists showed a clear preference in the following order: Haiami > Chucheong > Dasan 1, when using the paired comparison test, ranking test, and multiple comparison test. However, the untrained panelists showed no significant differences in their sensory ability to distinguish between the Haiami and Chucheong varieties when using the multiple comparison test. The results indicate that, for sensory evaluation of cooked rice by untrained panelists, the paired comparison test is suitable for evaluating two samples and the ranking test is suitable for evaluating more than two samples.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.59-68
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• 2022

A recommender system covers users, searches the items or services which users will like, and let users purchase them. Because recommendations from a recommender system are predictions of users' preferences for the items which they do not purchase yet, it is rarely possible to be drawn a perfect answer. An evaluation has been conducted to determine whether a prediction is right or not. However, it can be lower user's satisfaction if a recommender system focuses on only the preferences, that is caused by a 'filter bubble effect'. The filter bubble effect is an algorithmic bias that skews or limits the information an individual user sees on the recommended list. It is the reason why multiple metrics are required to evaluate recommender systems, and a diversity metrics is mainly used for it. In this paper, we compare three different methods for enhancing diversity for personalized recommendation - bin packing, weighted random choice, greedy re-ranking - with a practical e-commerce data acquired from a fashion shopping mall. Besides, we present the difference between experimental results and F1 scores.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.43
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• pp.321-331
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• 1997

Measurement of attributes is often highly subjective and imprecise, yet most MADM methods lack provisions for handling imprecise data. Frequently, decision makers must establish a ranking within a finite set of alternatives with respect to multiple attributes which have varying degrees of importance. The problem is more complex if the evaluations of alternatives according to each attribute are not expressed in precise numbers, but rather in fuzzy numbers. Analysis must allow for lack of precision and partial truth. The advantages of a fuzzy approach for MADM are that a decision maker can obtain efficient solutions all at once without trial and error, and that this approach provides better support for judging the interactive improvement of solutions in comparison with o decision making method. The algorithm used in this study is based on the concepts of vague set theory. Linguistic variables and vague values are used to facilitate a decision maker's subjective assessment about attribute weightings and the appropriateness of alternative versus selection attributes in order to obtain final scores which are called vague appropriateness indices. A numerical example is presented to show the practical applicability of this approach.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.1-10
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• 2022

Attempts to interpret human perspectives using computer vision have been developed in various fields. In this paper, we propose a method for evaluating the walking environment through semantic segmentation results of images from road images. First, the Kakao Map API was used to collect road images, and four-way images were collected from about 50,000 points in JeonJu. 20% of the collected images build datasets through crowdsourcing-based paired comparisons, and train various regression models using paired comparison data. In order to derive the walkability score of the image data, the ranking score is calculated using the Trueskill algorithm, which is a ranking algorithm, and the walkability and analysis using various regression models are performed using the constructed data. Through this study, it is shown that the walkability of Jeonju can be evaluated and scores can be derived through the correlation between pixel distribution classification information rather than human vision.

• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.369-380
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• 2004

In this paper, we propose a web page weighting scheme based on WordNet-based collaborative evaluation and hyperlink to improve the precision of web search engine. Generally search engines use keyword matching to decide web page ranking. In the information retrieval from huge data such as the Web, simple word comparison cannot distinguish important documents because there exist too many documents with similar relevancy. In this paper, we implement a WordNet-based user interface that helps to distinguish different senses of query word, and constructed a search engine in which the implicit evaluations by multiple users are reflected in ranking by accumulating the number of clicks. In accumulating click counts, they are stored separately according to lenses, so that more accurate search is possible. Weighting of each web page by using collaborative evaluation and hyperlink is reflected in ranking. The experimental results with several keywords show that the precision of proposed system is improved compared to conventional search engines.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.5-8
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• 2003

This paper presents a methodology to be able to select an appropriate RP system that suits the end use of a part. Evaluation factors used in process selection include major attributes such as accuracy, roughness, strength, elongation, part cost and build time that greatly affect the performance of RP systems. Crisp values such as accuracy and surface roughness are obtained with a new test part developed. The test part is designed with conjoint analysis to reflect users' preference. The part cost and build time that have approximate ranges due to cost and many variable parameters are presented by linguistic values that can be described with triangular fuzzy numbers. Based on the evaluation values obtained, an appropriate RP process for a specific part application is selected by using the modified TOPSIS(Technique of Order Preference by Similarity to Ideal Solution) method. It uses crisp data as well as linguistic variables, and each weight on the alternatives is assigned by using pair-wise comparison matrix. The ranking order helps the decision making of the selection of RP systems.

• Industrial Engineering and Management Systems
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• v.15 no.4
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• pp.364-373
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• 2016

Disassembly is one of the important activities in treating with the product at the End of Life time (EOL). Disassembly is defined as a systematic technique in dividing the products into its constituent elements, segments, sub-assemblies, and other groups. We concern with a Fuzzy Disassembly Line Balancing Problem (FDLBP) with multiple objectives in this article that it needs to allocation of disassembly tasks to the ordered group of disassembly Work Stations. Tasks-processing times are fuzzy numbers with triangular membership functions. Four objectives are acquired that include: (1) Minimization of number of disassembly work stations; (2) Minimization of sum of idle time periods from all work stations by ensuring from similar idle time at any work-station; (3) Maximization of preference in removal the hazardous parts at the shortest possible time; and (4) Maximization of preference in removal the high-demand parts before low-demand parts. This suggested model was initially solved by GAMS software and then using Genetic Algorithm (GA) in MATLAB software. This model has been utilized to balance automotive engine disassembly line in fuzzy environment. The fuzzy results derived from two software programs have been compared by ranking technique using mean and fuzzy dispersion with each other. The result of this comparison shows that genetic algorithm and solving it by MATLAB may be assumed as an efficient solution and effective algorithm to solve FDLBP in terms of quality of solution and determination of optimal sequence.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.2
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• pp.867-872
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• 2016

Background: Esophageal cancer is one of the most serious malignancies. Due to the aggressive nature of this cancer, the prognosis is poor. A network meta-analysis with simultaneous comparison of multiple treatments can help determine better treatment options that have higher effects on overall survival of patients with lower adverse events. The aim of this review is to simultaneously compare efficacy and adverse events of treatment interventions for esophageal cancer. Materials and Methods: In this review, only randomized control trials (RCT) will be considered for network meta-analysis. All international electronic databases including Medline, Web of Sciences, Scopus, Cochran's library, EMBASE and Cancerlit will be searched to find randomized control trials which compared two or more treatment interventions for esophageal cancer. A network plot will be drawn for visual representation of all available treatment interventions. Bayesian approach will be used to combine the direct and indirect evidence. Treatment effects (e.g. hazard ratio for time to event outcomes, risk ratio for binary outcomes, and rate ratio for count outcomes with 95% credible interval) will be reported. Moreover, cumulative probability of the treatment ranks will be reported using the surface under the cumulative ranking (SUCRA) graphs. Consistency assumption will be assessed by the loop-specific and design-by-treatment interaction approaches. Conclusions: The results of this study may be helpful for the patients, clinicians and health policy makers in selecting treatments that have the best effect on survival and lowest adverse events.