• 제목/요약/키워드: Classification Attributes

검색결과 303건 처리시간 0.029초

Kano 모델의 품질속성 분류를 위한 질문서 연구 (Comparing the Questionnaires for Classifying Quality Attributes in the Kano Model)

  • 김만호;송해근;박영택
    • 품질경영학회지
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    • 제41권2호
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    • pp.209-220
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    • 2013
  • Purpose: This paper compares and discusses the influence on the quality classification of Kano's questionnaire which is used for the Kano model(Kano et al., 1984), the 3-point Likert-scale newly proposed by Kano and the 5-point Likert-scale presented in this study. Methods: For the comparison, the current study conducts a survey of 631 television viewers. The classification results of the three methods are then compared with those of direct classification which is adopted as a standard for classification of quality attributes. Results: The agreement rates between the results using conventional Kano's questionnaire and the results using direct classification is higher than the results using 3-point and 5-point Likert-scales. In addition, the attributes grouped as must-be or attractive in the direct classification appear to be classified as one-dimensional attributes in the Likert-scales. Conclusion: In comparison with the convensional Kano's questionnaire, the Likert-scale questions highly tend to classify the quatity attributes as one-dimensional. Although the classification results of the 3-point and 5-point Likert-scales are the same, the 5-point Likert-scale has the advantage to classify quality attributes in more detail.

사용자 요구품질 추출과 분류방법의 개선에 관한 연구 (A Study For the Development of Enhanced Classification Method of Consumer Attributes)

  • 김승남;김철홍;정영배;김연수
    • 산업경영시스템학회지
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    • 제24권67호
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    • pp.77-82
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    • 2001
  • A study was conducted to develop a better classification method of Consumer Attributes that can enhance user-centered product design process. A modified QFD(Quality Function Deployment) survey form based upon Fuzzy set theory was proposed which contains 9 steps of importance level, and Certainty and Necessity function to improve the reliability of extracted consumer attributes. To verify the betterment and advantage of proposed classification method, a series of questionnaire survey was performed. Thirty male and 30 female university students were participated in the survey using a VCR as a target product. The result of the study showed that 80% of subjects were preferred the proposed classification over existing method. A cluster analysis was performed to further verify the betterment of the proposed method. The result also supported that the proposed classification method is more reliable and enhanced method in extracting consumer attributes and can be applied in the product design.

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Rough Set-Based Approach for Automatic Emotion Classification of Music

  • Baniya, Babu Kaji;Lee, Joonwhoan
    • Journal of Information Processing Systems
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    • 제13권2호
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    • pp.400-416
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    • 2017
  • Music emotion is an important component in the field of music information retrieval and computational musicology. This paper proposes an approach for automatic emotion classification, based on rough set (RS) theory. In the proposed approach, four different sets of music features are extracted, representing dynamics, rhythm, spectral, and harmony. From the features, five different statistical parameters are considered as attributes, including up to the $4^{th}$ order central moments of each feature, and covariance components of mutual ones. The large number of attributes is controlled by RS-based approach, in which superfluous features are removed, to obtain indispensable ones. In addition, RS-based approach makes it possible to visualize which attributes play a significant role in the generated rules, and also determine the strength of each rule for classification. The experiments have been performed to find out which audio features and which of the different statistical parameters derived from them are important for emotion classification. Also, the resulting indispensable attributes and the usefulness of covariance components have been discussed. The overall classification accuracy with all statistical parameters has recorded comparatively better than currently existing methods on a pair of datasets.

Kano 모델의 설문 워딩에 관한 연구 (Wordings of the Kano Model's Questionnaire)

  • 송해근;박영택
    • 품질경영학회지
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    • 제40권4호
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    • pp.453-466
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    • 2012
  • Purpose: The Kano model has been widely accepted as a method for classifying quality attributes for almost three decades since its introduction. However, the wordings of the five alternatives in the Kano's questionnaire has been criticised for unclear and confusable meanings. New wordings of the five alternatives are proposed in this paper. Methods: To evaluate the effectiveness of the proposed wordings, we classify 30 quality attributes of smartphones using the conventional wordings and the proposed wordings respectively. The two classification results are compared with the direct classification results by undergraduate students who learned the Kano model. Results: The classification results using the proposed wordings are much more consistent with the direct classification results than those using the conventional wordings. Conclusion: The proposed wordings are less confusable and easy to understand, and thus it results in more consistent with the direct classification.

Evaluation of Attribute Selection Methods and Prior Discretization in Supervised Learning

  • Cha, Woon Ock;Huh, Moon Yul
    • Communications for Statistical Applications and Methods
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    • 제10권3호
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    • pp.879-894
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    • 2003
  • We evaluated the efficiencies of applying attribute selection methods and prior discretization to supervised learning, modelled by C4.5 and Naive Bayes. Three databases were obtained from UCI data archive, which consisted of continuous attributes except for one decision attribute. Four methods were used for attribute selection : MDI, ReliefF, Gain Ratio and Consistency-based method. MDI and ReliefF can be used for both continuous and discrete attributes, but the other two methods can be used only for discrete attributes. Discretization was performed using the Fayyad and Irani method. To investigate the effect of noise included in the database, noises were introduced into the data sets up to the extents of 10 or 20%, and then the data, including those either containing the noises or not, were processed through the steps of attribute selection, discretization and classification. The results of this study indicate that classification of the data based on selected attributes yields higher accuracy than in the case of classifying the full data set, and prior discretization does not lower the accuracy.

수정된 이원평가표를 이용한 품질속성의 분류에 관한 연구 (Classification of Quality Attributes Using Two-dimensional Evaluation Table)

  • 김광필;송해근
    • 대한안전경영과학회지
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    • 제20권1호
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    • pp.41-55
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    • 2018
  • For several decades, attribute classification methods using the asymmetrical relationship between an attribute performance and the satisfaction of that attribute have been explored by numerous researchers. In particular, the Kano model, which classifies quality attributes into 5 elements using simple questionnaire and two-dimensional evaluation table, has gained popularity: Attractive, One-dimensional, Must-be, Indifferent, and Reverse quality. As Kano's model is well accepted, many literatures have introduced categorization methods using the Kano's evaluation table at attribute level. However, they applied different terminologies and classification criteria and this causes confusion and misunderstanding. Therefore, a criterion for quality classification at attribute level is necessary. This study is aimed to suggest a new attribute classification method that sub-categorizes quality attributes using 5-point ordinal point and Kano's two-dimensional evaluation table through an extensive literature review. For this, the current study examines the intrinsic and extrinsic problems of the well-recognized Kano model that have been used for measuring customer satisfaction of products and services. For empirical study, the author conducted a comparative study between the results of Kano's model and the proposed method for an e-learning case (33 attributes). Results show that the proposed method is better in terms of ease of use and understanding of kano's results and this result will contribute to the further development of the attractive quality theory that enables to understand both the customers explicit and implicit needs.

물류설비 및 속성 분류체계 연구 (A Classification Study on Logistics Equipments and Their Attributes)

  • 장태우
    • 한국철도학회논문집
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    • 제12권1호
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    • pp.175-182
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    • 2009
  • 포장, 수송, 운반, 하역, 보관 등 물류 기능별 단계에서 사용되는 설비의 호환성과 연계성에 대한 요구가 지속되고 있다. 본 연구에서는 일관수송을 위한 표준 파렛트를 중심으로 물류설비 간의 모자 관계 등에 대한 인터페이스 분석을 위한 분류체계를 제시하고자 한다. 이를 위해 국내외 물류설비 관련 분류체계 및 표준/규격을 검토하여 문제점과 이슈를 분석하였다. 또한 설비별 속성 분류체계를 제시하고 의미론적으로 표현하는 예를 제시한다. 본 연구의 결과를 확장하여 표준 파렛트의 변경 또는 다변화에 대비한 관련 물류설비의 정합성 검토의 자동화 작업이 가능할 것이다.

딥러닝 기반 농경지 속성분류를 위한 TIF 이미지와 ECW 이미지 간 정확도 비교 연구 (A Study on the Attributes Classification of Agricultural Land Based on Deep Learning Comparison of Accuracy between TIF Image and ECW Image)

  • 김지영;위성승
    • 한국농공학회논문집
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    • 제65권6호
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    • pp.15-22
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    • 2023
  • In this study, We conduct a comparative study of deep learning-based classification of agricultural field attributes using Tagged Image File (TIF) and Enhanced Compression Wavelet (ECW) images. The goal is to interpret and classify the attributes of agricultural fields by analyzing the differences between these two image formats. "FarmMap," initiated by the Ministry of Agriculture, Food and Rural Affairs in 2014, serves as the first digital map of agricultural land in South Korea. It comprises attributes such as paddy, field, orchard, agricultural facility and ginseng cultivation areas. For the purpose of comparing deep learning-based agricultural attribute classification, we consider the location and class information of objects, as well as the attribute information of FarmMap. We utilize the ResNet-50 instance segmentation model, which is suitable for this task, to conduct simulated experiments. The comparison of agricultural attribute classification between the two images is measured in terms of accuracy. The experimental results indicate that the accuracy of TIF images is 90.44%, while that of ECW images is 91.72%. The ECW image model demonstrates approximately 1.28% higher accuracy. However, statistical validation, specifically Wilcoxon rank-sum tests, did not reveal a significant difference in accuracy between the two images.

데이터기반 의사결정을 위한 정책 및 사업 속성 분류체계 개발 연구 (A Study on Development of Policy Attributes Taxonomy for Data-based Decision Making)

  • 김사랑
    • 한국정보시스템학회지:정보시스템연구
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    • 제29권3호
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    • pp.1-34
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    • 2020
  • Purpose Due to the complexity of policy environment in modern society, it is accepted as common basics of policy design to mix up a variety of policy instruments aiming the multiple functions. However, under the current situation of written-down policy specification, not only the public officers but also the policy researchers cannot easily grasp such frameworks as policy portfolio. The purpose of this study is to develop "Policy Attributes Taxonomy" identifying and classifying the public programs to help making decisions for allocative efficiency with effectiveness-based information. Design/methodology/approach To figure out the main scheme and classification criteria of Policy Attributes Taxonomy which represents characteristics of public policies, previous theories and researches on policy components were explored. In addition, to test taxonomic feasibility of certain information system, a set of "Feasibility Standards" was drawn from "requirements for well-organized criteria" of eminent taxonomy literatures. Finally, current government classification system in the area of social service was tested to visualize the application of Taxonomy and Standards. Findings Program Taxonomy Schemes were set including "policy goals", "policy targets", "policy tools", "logical relation" and "delivery system". Each program and project could be condensed into these attributes, making their design more easily distinguishable. Policy portfolio could be readily made out by extracting certain characteristics according to this scheme. Moreover, this taxonomy could be used for rearrangement of present "Program Budget System" or estimation of "Basic Income".

학교급식 식재료 분류 및 품질속성체계 표준화 방안 연구 (Standardization of Ingredient Classification and Quality Attributes of at School Foodservices)

  • 김재민;김창식;장윤정;함선옥
    • 대한영양사협회학술지
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    • 제23권4호
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    • pp.453-463
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    • 2017
  • The purpose of this study was to standardize ingredients used by school foodservices. This study analyzed the current notation of ingredients in used by used in school foodservices through the NEIS system employed by school foodservices of elementary schools through high schools in South Korea. Specifically, this study suggests systemized standardization of ingredient classification and quality attributes of at school foodservices by applying a case study analysis. The findings from the case analysis of the Electronic Procurement System operator are as follows. Classifications for ingredients of the NEIS system used by school food services consisted of included food group, food name, detailed food name, and description. Classification was not clearly divided between the classification scheme and the attribute system. Therefore, food group, food name, and product information of each food should be categorized as the classification scheme, whereas the detailed food name (excluding product information) and description should be standardized as the attribute system, which is composed of required attributes, recommended attributes, and other attributes. This study suggests that system standardization should be carried out in the field of school foodservices, as advancements between distributors and school food service providers could affect food ingredient quality. Thus, standardization can influence purchase and distribution in many ways.