• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.75-80
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• 2001

Rule inconsistency is an important issue that is needed to be addressed while designing efficient and optimal fuzzy rule bases. Automatic generation of fuzzy rules from data sets, using machine learning techniques, can generate a significant number of redundant and inconsistent rules. In this study we have shown that it is possible to provide a systematic approach to understand the fuzzy rule inconsistency problem by using the proposed measure called the Commonality measure. Apart from introducing this measure, this paper describes an algorithm to optimize a fuzzy rule base using it. The optimization procedure performs elimination of redundant and/or inconsistent fuzzy rules from a rule base.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.292-297
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• 2010

In this paper, we introduce the notions of inconsistency p-measure, consistency p-measure and fuzziness p-measure. We discuss various properties of them. We investigate the degree of inconsistency and roughness p-measure in a decision table.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.276-283
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• 2007

It has been shown that the structure identification of a neuro-fuzzy model improves their accuracy performances in a various modeling problems. In this paper, we claim that the structure identification of a neuro-fuzzy model can also reduce the degree of inconsistency of its fuzzy rulebase. Thus, the resulting neuro-fuzzy model serves as more like a structured knowledge representation scheme. For this, we briefly review a structure identification method of a neuro-fuzzy model and propose a systematic method to measure inconsistency of a fuzzy rulebase. The proposed method is applied to problems or fuzzy system reproduction and nonlinear system modeling in order to validate our claim.

• Journal of the Korean Society of Clothing and Textiles
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• v.22 no.5
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• pp.617-627
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• 1998

In marketing reserach, the ultimate goal is to increase predictability of consumer's purchse behavior. However, most of the marketing researchers measure purchase intention rather than behavior assuming that the consumer's purchase behaviors will coincide with their intentions. Lately, there have been many arguments whether purchase intention is meaningful as a determinant or a predictor for purchase behavior. Additionally, many studies reported substantial variations among the 'product categories. The purpose of this study was to find out the relationship between consumer's purchase intention and purchase behavior of apparel products. Two research questions were set up. One was to find out the relationship between the intention and behavior, and the other was to find out the factors affecting the relationship. It was found out that mere intention could neither explain nor predict behavior, and that the factors affecting intenting intention- behavior inconsistency had to be submitted for explanation and prediction. These factors could explain inconsistency between the intention and the behavior. Regression equations in the past researches explained aggregate results, but coludn't explain each consumer's intention-behavior inconsistency. The integration of the specific intention and the affecting factors can increase predictability of each consumer's purchase behavior.

• Proceedings of the Safety Management and Science Conference
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• 2006.11a
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• pp.427-435
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• 2006

We study on the consistency of AHP. It is research that extend of SAW methods by [1]. For tools that measure judgment of inconsistency eigenvector methods, we research consistency that introduced consistency ratio by Saaty. in general, the higher consistence of compare matrix the bigger error within matrix. In this paper, we use the AHP for the optimal decision making. By this method, we have optimal decision making numenical example which three models of any domestic motors companies.

• Journal of Intelligence and Information Systems
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• v.14 no.4
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• pp.179-200
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• 2008

We develop a feature selection method that can improve both the efficiency and the effectiveness of classification technique. In this research, we employ case-based reasoning as a classification technique. Basically, this research integrates the two existing feature selection methods, i.e., the univariate analysis and the LVF algorithm. First, we sift some predictive features from the whole set of features using the univariate analysis. Then, we generate all possible subsets of features from these predictive features and measure the inconsistency rate of each subset using the LVF algorithm. Finally, the subset having the lowest inconsistency rate is selected as the best subset of features. We measure the performances of our feature selection method using the data obtained from UCI Machine Learning Repository, and compare them with those of existing methods. The number of selected features and the accuracy of our feature selection method are so satisfactory that the improvements both in efficiency and effectiveness are achieved.

• Journal of KIISE:Software and Applications
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• v.27 no.1
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• pp.13-23
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• 2000

As software becomes more complicated and large-scaled, user's demands become more varied and his expectation levels about software products are raised. Therefore it is very important that a software engineer analyzes user's requirements precisely and applies it effectively in the development step. This paper presents a requirements analysis system that reduces and revises errors of requirements specifications analysis effectively. As this system measures the similarity among requirements documents and sentences, it assists users in analyzing the dependency among requirements specifications and finding the traceability, redundancy, inconsistency and incompleteness among requirements sentences. It also extracts sentences that contain ambiguous words. Indexing method for the similarity measurement combines sliding window model and dependency structure model. This method can complement each model's weeknesses. This paper verifies the efficiency of similarity measure techniques through experiments and presents a proccess of the requirements specifications analysis using the embodied system.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2865-2872
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• 2018

In the field of data mining technique, there are various methods such as association rules, cluster analysis, decision tree, neural network. Among them, association rules are defined by using various association evaluation criteria such as support, confidence, and lift. Agrawal et al. (1993) first proposed this association rule, and since then research has been conducted by many scholars. Recently, studies related to crossover entropy have been published (Park, 2016b). In this paper, we proposed a purely symmetric J measure considering directionality and purity in the previously published J measure, and examined its usefulness by using examples. As a result, it is found that the pure symmetric J measure changes more clearly than the conventional J measure, the symmetric J measure, and the pure crossover entropy measure as the frequency of coincidence increases. The variation of the pure symmetric J measure was also larger depending on the magnitude of the inconsistency, and the presence or absence of the association was more clearly understood.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.9
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• pp.906-908
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• 2009

AVI (Automatic Vision Inspection) systems automatically detect defect features and measure their sizes via camera vision. It is important to predict the performance of an AVI to meet customer's specification in advance. Also the prediction can indicate the level of current performance of an AVI system. In this paper, we propose a statistical method for prediction of false alarm rate regarding inconsistency of defect size measurement process. For this purpose, only simple experiments are needed to measure the defect sizes for certain number of times. The statistical features from the experiment are utilized in the prediction process. Therefore, the proposed method is swift and easy to implement and use. The experiment shows a close prediction compared to manual inspection results.

• Journal of Advanced Marine Engineering and Technology
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• v.26 no.3
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• pp.382-388
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• 2002

Dissolved silica is one of fatal components at a boiler facility Therefore, a dissolved silica measurement system should be equipped for managing efficiently the boiler facility. Most of silica measurement systems are composed of a sensor module of single-beam type structure, and silica density is measured with a infrared spectrometry using the Lambert-beer method. However, such a system occurs measuring error of large range and inconsistency of a light source, because of measuring a standard sample and a measuring sample alternatively. This paper introduces a method that the sensor module has a split-beam type structure and a tungsten lamp. The proposed system can measure silica density quickly and precisely more than those composing of a single-beam type structure, because of measuring and comparing with two samples at a same time. And examination results are shown to compare efficiencies of the system and existing commercial products, and for an ammonia influence.