• Title/Summary/Keyword: Feature Importance Analysis

Search Result 139, Processing Time 0.021 seconds

An Empirical Study on the Vendor's Opportunism in the Collaboration between Buyer and Vendor

  • Hwang, Sunil;Suh, Eung-Kyo
    • The Journal of Industrial Distribution & Business
    • /
    • v.8 no.5
    • /
    • pp.53-63
    • /
    • 2017
  • Purpose - The main feature of this study is understanding of the vendor's opportunism on the collaboration context between buyer and vendor from the buyer's viewpoint with resource dependence theory. A number of studies on opportunism have focused on opportunistic definitions and its theoretical studies. Other researches emphasize the importance of governance in ways that reduce opportunism. We think that this research could be filled with the lack of previous studies. Research design, data, and methodology - In order to accomplish research purpose, four hypotheses have been established based on the framework of resource dependence theory and previous studies. And we have used 599 survey data jointly collected by Korea Productivity Center and the Ministry of Trade, Industry and Energy. To verify these hypothesis, we have conducted multiple regression analysis with SPSS 23.0. Results - The vendor 's opportunism decreases as mutual trust with buyer becomes higher. However, as the degree of dependence of buyers on vendor resources increases, vendor's opportunism increases. And monitoring vendor's capacity has a moderating effect with buyer resource dependency to vendor's opportunism. Conclusions - This study suggest there are two options to decrease vendor's opportunism. Increasing mutual trust or decrease dependence on vendor's resources. Also, monitoring suppler's capacity could be effective when vendor's resource dependence is high.

A Design of Context Prediction Structure using Homogeneous Feature Extraction (동질적 특징추출을 이용한 상황예측 구조의 설계)

  • Kim, Hyung-Sun;Im, Kyoung-Mi;Lim, Jae-Hyun
    • Journal of Internet Computing and Services
    • /
    • v.11 no.4
    • /
    • pp.85-94
    • /
    • 2010
  • In this paper, we propose a location-prediction structure that can provide user service in advance. It consists of seven steps and supplies intelligent services which can forecast user's location. Context information collected from physical sensors and a history database is so difficult that it can't present importance of data and abstraction of data because of heterogeneous data type. Hence, we offer the location-prediction that change data type from heterogeneous data to homogeneous data. Extracted data is clustered by SOFM, then it gets user's location information by ARIMA and realizes the services by a reasoning engine. In order to validate the proposed location-prediction, we built a test-bed and test it by the scenario.

Effect Analysis of Learning Using Educational Contents (교육용 콘텐츠를 활용한 수업의 효과 분석)

  • Park Hye-Yeong;Kho Dae-Ghon;Ahn Seong-Hun
    • The Journal of the Korea Contents Association
    • /
    • v.5 no.6
    • /
    • pp.293-300
    • /
    • 2005
  • The importance of ICT is being emphasized in this information-oriented society. In the 7th curriculum, it is necessary to use ICT more than 10% in teaching every subjects to keep pace with this trend. The distinctive feature of the curriculum lies in the introduction of various gradations and levels of the individual students. In this paper, by designing and applying the teaching idea based on educational contents, we analyzed how it has an effect on achieving of studies. As the results of this research, we found that teaching based on educational contents made students the meaningful increasement of interest in the controlled group and according to the increasement of the achievement in studying, the improvement of teaching are confirmed.

  • PDF

Developing of New a Tensorflow Tutorial Model on Machine Learning : Focusing on the Kaggle Titanic Dataset (텐서플로우 튜토리얼 방식의 머신러닝 신규 모델 개발 : 캐글 타이타닉 데이터 셋을 중심으로)

  • Kim, Dong Gil;Park, Yong-Soon;Park, Lae-Jeong;Chung, Tae-Yun
    • IEMEK Journal of Embedded Systems and Applications
    • /
    • v.14 no.4
    • /
    • pp.207-218
    • /
    • 2019
  • The purpose of this study is to develop a model that can systematically study the whole learning process of machine learning. Since the existing model describes the learning process with minimum coding, it can learn the progress of machine learning sequentially through the new model, and can visualize each process using the tensor flow. The new model used all of the existing model algorithms and confirmed the importance of the variables that affect the target variable, survival. The used to classification training data into training and verification, and to evaluate the performance of the model with test data. As a result of the final analysis, the ensemble techniques is the all tutorial model showed high performance, and the maximum performance of the model was improved by maximum 5.2% when compared with the existing model using. In future research, it is necessary to construct an environment in which machine learning can be learned regardless of the data preprocessing method and OS that can learn a model that is better than the existing performance.

Comparison Analysis of Machine Learning for Concrete Crack Depths Prediction Using Thermal Image and Environmental Parameters (열화상 이미지와 환경변수를 이용한 콘크리트 균열 깊이 예측 머신 러닝 분석)

  • Kim, Jihyung;Jang, Arum;Park, Min Jae;Ju, Young K.
    • Journal of Korean Association for Spatial Structures
    • /
    • v.21 no.2
    • /
    • pp.99-110
    • /
    • 2021
  • This study presents the estimation of crack depth by analyzing temperatures extracted from thermal images and environmental parameters such as air temperature, air humidity, illumination. The statistics of all acquired features and the correlation coefficient among thermal images and environmental parameters are presented. The concrete crack depths were predicted by four different machine learning models: Multi-Layer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB). The machine learning algorithms are validated by the coefficient of determination, accuracy, and Mean Absolute Percentage Error (MAPE). The AB model had a great performance among the four models due to the non-linearity of features and weak learner aggregation with weights on misclassified data. The maximum depth 11 of the base estimator in the AB model is efficient with high performance with 97.6% of accuracy and 0.07% of MAPE. Feature importances, permutation importance, and partial dependence are analyzed in the AB model. The results show that the marginal effect of air humidity, crack depth, and crack temperature in order is higher than that of the others.

Machine learning-based analysis and prediction model on the strengthening mechanism of biopolymer-based soil treatment

  • Haejin Lee;Jaemin Lee;Seunghwa Ryu;Ilhan Chang
    • Geomechanics and Engineering
    • /
    • v.36 no.4
    • /
    • pp.381-390
    • /
    • 2024
  • The introduction of bio-based materials has been recommended in the geotechnical engineering field to reduce environmental pollutants such as heavy metals and greenhouse gases. However, bio-treated soil methods face limitations in field application due to short research periods and insufficient verification of engineering performance, especially when compared to conventional materials like cement. Therefore, this study aimed to develop a machine learning model for predicting the unconfined compressive strength, a representative soil property, of biopolymer-based soil treatment (BPST). Four machine learning algorithms were compared to determine a suitable model, including linear regression (LR), support vector regression (SVR), random forest (RF), and neural network (NN). Except for LR, the SVR, RF, and NN algorithms exhibited high predictive performance with an R2 value of 0.98 or higher. The permutation feature importance technique was used to identify the main factors affecting the strength enhancement of BPST. The results indicated that the unconfined compressive strength of BPST is affected by mean particle size, followed by biopolymer content and water content. With a reliable prediction model, the proposed model can present guidelines prior to laboratory testing and field application, thereby saving a significant amount of time and money.

Customer Satisfaction Analysis of Smart Car Features Using the Kano Model: in Control Effect of the Comprehension or Experience of Emerging Technologies (Kano모형을 기반으로 한 스마트 카 기능의 고객 만족도 분석: 신기술 사용경험 유무의 조절효과 중심으로)

  • Kang, Young Tai;Chung, Kyu Suk
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
    • /
    • v.13 no.4
    • /
    • pp.155-168
    • /
    • 2018
  • This study singled out 30 smart car features and surveyed 250 respondents. Assuming that the relationship between fulfillment of a feature or a customer need and the satisfaction with that feature is not necessarily linear, this study was conducted using Kano's method. Two devices, Timko Deviation(TD) and Kano Distribution Index(KDI), were devised to help evaluate resulting Kano table quantitatively. Previous research based on Kano's original framework showed the limit to the analysis of new or unfamiliar features: more than 85% of the features surveyed turned out to be either Attractive or Indifferent attributes. This study attempted a new empirical approach by applying customer experiences, price conditions, and customer self-stated importance. The results showed that customer experience of the surveyed features affected the overall satisfaction level, signifying that Kano's method should be conducted with care when analyzing emerging technologies such as smart cars. It is expected that this study would be utilized for better understanding of the perception and trends of customers regarding new technologies. This study also suggests a new approach to the analysis of customer requirements by providing price conditions.

Finite Element Analysis of Powder Injection Molding Filling Process Including Yield Stress and Slip Phenomena (항복응력과 미끄럼현상을 고려한 분말사출성형 충전공정의 유한요소해석)

  • 박주배;권태헌
    • Transactions of the Korean Society of Mechanical Engineers
    • /
    • v.17 no.6
    • /
    • pp.1465-1477
    • /
    • 1993
  • Powder Injection Molding(PM) is an advanced and complicated technology for manufacturing ceramic or metal products making use of a conventional injection molding process, which is generally used for plastic products. Among many technologies involved in the successful PIM, injection molding process is one of the key steps to form a desired shape out of powder/binder mixtures. Thus, it is of great importance to have a numerical tool to predict the powder injection molding filling process. In this regard, a finite element analysis system has been developed for numerical simulations of filling process of powder injection molding. Powder/polymer mixtures during the filling pro cess of injection molding can be rheologically characterized as Non-Newtonian fluids with a so called yield phenomena and have a peculiar feature of apparent slip phenomena on the wall boundaries surrounding mold cavity. Therefore, in the present study, a physical modeling of the filling process of powder/polymer mixtures was developed to take into account both the yield stress and slip phenomena and a finite element formulation was developed accordingly. The numerical analysis scheme for filling simulation is accomplished by combining a finite element method with control volume technique to simulate the movement of flow front and a finite difference method to calculate the temperature distribution. The present study presents the modeling, numerical scheme and some numerical analysis results showing the effect of the yield stress and slip phenomena.

Categorizing Quality Features of Franchisees: In the case of Korean Food Service Industry (프랜차이즈 매장 품질요인의 속성분류: 국내 외식업을 중심으로)

  • Byun, Sook-Eun;Cho, Eun-Seong
    • Journal of Distribution Research
    • /
    • v.16 no.1
    • /
    • pp.95-115
    • /
    • 2011
  • Food service is the major part of franchise business in Korea, accounting for 69.9% of the brands in the market. As the food service industry becomes mature, many franchisees have struggled to survive in the market. In general, consumers have higher levels of expectation toward service quality of franchised outlets compared that of (non-franchised) independent ones. They also tend to believe that franchisees deliver standardized service at the uniform food price, regardless of their locations. Such beliefs seem to be important reasons that consumers prefer franchised outlets to independent ones. Nevertheless, few studies examined the impact of qualify features of franchisees on customer satisfaction so far. To this end, this study examined the characteristics of various quality features of franchisees in the food service industry, regarding their relationship with customer satisfaction and dissatisfaction. The quality perception of heavy-users was also compared with that of light-users in order to find insights for developing differentiated marketing strategy for the two segments. Customer satisfaction has been understood as a one-dimensional construct while there are recent studies that insist two-dimensional nature of the construct. In this regard, Kano et al. (1984) suggested to categorize quality features of a product or service into five types, based on their relation to customer satisfaction and dissatisfaction: Must-be quality, Attractive quality, One-dimensional quality, Indifferent quality, and Reverse quality. According to the Kano model, customers are more dissatisfied when Must-be quality(M) are not fulfilled, but their satisfaction does not arise above neutral no matter how fully the quality fulfilled. In comparison, customers are more satisfied with a full provision of Attactive quality(A) but manage to accept its dysfunction. One-dimensional quality(O) results in satisfaction when fulfilled and dissatisfaction when not fulfilled. For Indifferent quality(I), its presence or absence influences neither customer satisfaction nor dissatisfaction. Lastly, Reverse quality(R) refers to the features whose high degree of achievement results in customer dissatisfaction rather than satisfaction. Meanwhile, the basic guidelines of the Kano model have a limitation in that the quality type of each feature is simply determined by calculating the mode statistics. In order to overcome such limitation, the relative importance of each feature on customer satisfaction (Better value; b) and dissatisfaction (Worse value; w) were calculated following the formulas below (Timko, 1993). The Better value indicates how much customer satisfaction is increased by providing the quality feature in question. In contrast, the Worse value indicates how much customer dissatisfaction is decreased by providing the quality feature. Better = (A + O)/(A+O+M+I) Worse = (O+M)/(A+O+M+I)(-1) An on-line survey was performed in order to understand the nature of quality features of franchisees in the food service industry by applying the Kano Model. A total of twenty quality features (refer to the Table 2) were identified as the result of literature review in franchise business and a pre-test with fifty college students in Seoul. The potential respondents of our main survey was limited to the customers who have visited more than two restaurants/stores of the same franchise brand. Survey invitation e-mails were sent out to the panels of a market research company and a total of 257 responses were used for analysis. Following the guidelines of Kano model, each of the twenty quality features was classified into one of the five types based on customers' responses to a set of questions: "(1) how do you feel if the following quality feature is fulfilled in the franchise restaurant that you visit," and "(2) how do you feel if the following quality feature is not fulfilled in the franchise restaurant that you visit." The analyses revealed that customers' dissatisfaction with franchisees is commonly associated with the poor level of cleanliness of the store (w=-0.872), kindness of the staffs(w=-0.890), conveniences such as parking lot and restroom(w=-0.669), and expertise of the staffs(w=-0.492). Such quality features were categorized as Must-be quality in this study. While standardization or uniformity across franchisees has been emphasized in franchise business, this study found that consumers are interested only in uniformity of price across franchisees(w=-0.608), but not interested in standardizations of menu items, interior designs, customer service procedures, and food tastes. Customers appeared to be more satisfied when the franchise brand has promotional events such as giveaways(b=0.767), good accessibility(b=0.699), customer loyalty programs(b=0.659), award winning history(b=0.641), and outlets in the overseas market(b=0.506). The results are summarized in a matrix form in Table 1. Better(b) and Worse(w) index indicate relative importance of each quality feature on customer satisfaction and dissatisfaction, respectively. Meanwhile, there were differences in perceiving the quality features between light users and heavy users of any specific franchise brand in the food service industry. Expertise of the staffs was labeled as Must-be quality for heavy users but Indifferent quality for light users. Light users seemed indifferent to overseas expansion of the brand and offering new menu items on a regular basis, while heavy users appeared to perceive them as Attractive quality. Such difference may come from their different levels of involvement when they eat out. The results are shown in Table 2. The findings of this study help practitioners understand the quality features they need to focus on to strengthen the competitive power in the food service market. Above all, removing the factors that cause customer dissatisfaction seems to be the most critical for franchisees. To retain loyal customers of the franchise brand, it is also recommended for franchisor to invest resources in the development of new menu items as well as training programs for the staffs. Lastly, if resources allow, promotional events, loyalty programs, overseas expansion, award-winning history can be considered as tools for attracting more customers to the business.

  • PDF

Studying the Comparative Analysis of Highway Traffic Accident Severity Using the Random Forest Method. (Random Forest를 활용한 고속도로 교통사고 심각도 비교분석에 관한 연구)

  • Sun-min Lee;Byoung-Jo Yoon;WutYeeLwin
    • Journal of the Society of Disaster Information
    • /
    • v.20 no.1
    • /
    • pp.156-168
    • /
    • 2024
  • Purpose: The trend of highway traffic accidents shows a repeating pattern of increase and decrease, with the fatality rate being highest on highways among all road types. Therefore, there is a need to establish improvement measures that reflect the situation within the country. Method: We conducted accident severity analysis using Random Forest on data from accidents occurring on 10 specific routes with high accident rates among national highways from 2019 to 2021. Factors influencing accident severity were identified. Result: The analysis, conducted using the SHAP package to determine the top 10 variable importance, revealed that among highway traffic accidents, the variables with a significant impact on accident severity are the age of the perpetrator being between 20 and less than 39 years, the time period being daytime (06:00-18:00), occurrence on weekends (Sat-Sun), seasons being summer and winter, violation of traffic regulations (failure to comply with safe driving), road type being a tunnel, geometric structure having a high number of lanes and a high speed limit. We identified a total of 10 independent variables that showed a positive correlation with highway traffic accident severity. Conclusion: As accidents on highways occur due to the complex interaction of various factors, predicting accidents poses significant challenges. However, utilizing the results obtained from this study, there is a need for in-depth analysis of the factors influencing the severity of highway traffic accidents. Efforts should be made to establish efficient and rational response measures based on the findings of this research.