• Journal of the Korean Society of Food Science and Nutrition
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• v.34 no.2
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• pp.202-208
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• 2005

This study was conducted to examine the food habits, knowledge of nutrition and actual conditions of food ingestion of adolescent middle school students according to questionnaire answers. Questionnaires were completed by 524 students, divided into a healthy group (n=289) and an unhealthy group (n=235) according to clinical signs. Further questions were asked of the two groups in the areas of food habits, knowledge of nutrition and nutritional attitude. The results were as follows: Mean age of all subjects was 14, heights for male and female students were 162.0 em, and 157.2 cm, weights were 53.4 kg, and 49.4, respectively. Heights and weights of male students were greater than those of female students. The body mass index (BMI) for male and female students was 20.3 kg/$m^2$ and 20.0 kg/$m^2$, respectively, and all data were within normal ranges. There were no significant differences in mean age, height, weight, and BMI between the healthy and unhealthy groups. There was no significant difference in body image recognition between the two groups, although the ratio of dissatisfaction with their own body shape was significantly higher in the female unhealthy group (46.1%), than in the female healthy group (33.0%) (p<0.05). In the area of the struggle to control body weight during the previous year, the female unhealthy group (59.4%) was higher than the female healthy group (38.4%) (p<0.01). There was no significant difference in the scores between the two groups in the areas of knowledge of nutrition and the nutritional attitude. Meal frequency and meal patterns were showed that having breakfast less than 4x/week was significantly higher in the female unhealthy group (44.0%), than in the female healthy group (30.7%) (p<0.01). Meal frequency for suppers<4x/week showed that the female unhealthy group (18.8%) was also higher than the female healthy group (10.7%). Therefore, the unhealthy group exhibited a higher pattern of missing both breakfast and supper. The male unhealthy group (16.7%) dined out more frequently than the male healthy group (12.3%) (p<0.01), and female unhealthy group also indulged in snacking significantly more frequently than the female healthy group. The unhealthy group also ate only 1 item for meals more frequently than the healthy group and no significant difference. The conclusion of this study is that adolescent Korean middle school students, who showed a higher incidence of clinical symptoms, representing an unhealthy status, missed breakfast and supper, and dined out and indulged in snacking more frequently. Their quality of breakfast and satisfaction of body image were also lower than the healthy group. These results indicated that there is a high correlation between a Korean adolescent's health status, food habits and body image satisfaction. It is recommended that a more intense program of nutritional education and monitoring be introduce into the current Korean middle-school system in order to optimally support and maximize the health potential of the current population of Korean student.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of Distribution Research
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• v.15 no.3
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• pp.71-110
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• 2010

1. Introduction Today Internet is recognized as an important way for the transaction of products and services. According to the data surveyed by the National Statistical Office, the on-line transaction in 2007 for a year, 15.7656 trillion, shows a 17.1%(2.3060 trillion won) increase over last year, of these, the amount of B2C has been increased 12.0%(10.2258 trillion won). Like this, because the entry barrier of on-line market of Korea is low, many retailers could easily enter into the market. So the bigger its scale is, but on the other hand, the tougher its competition is. Particularly due to the Internet and innovation of IT, the existing market has been changed into the perfect competitive market(Srinivasan, Rolph & Kishore, 2002). In the early years of on-line business, they think that the main reason for success is a moderate price, they are awakened to its importance of on-line service quality with tough competition. If it's not sure whether customers can be provided with what they want, they can use the Web sites, perhaps they can trust their products that had been already bought or not, they have a doubt its viability(Parasuraman, Zeithaml & Malhotra, 2005). Customers can directly reserve and issue their air tickets irrespective of place and time at the Web sites of travel agencies or airlines, but its empirical studies about these Web sites for reserving and issuing air tickets are insufficient. Therefore this study goes on for following specific objects. First object is to measure service quality and service recovery of Web sites for reserving and issuing air tickets. Second is to look into whether above on-line service quality and on-line service recovery have an impact on overall service quality. Third is to seek for the relation with overall service quality and customer satisfaction, then this customer satisfaction and loyalty intention. 2. Theoretical Background 2.1 On-line Service Quality Barnes & Vidgen(2000; 2001a; 2001b; 2002) had invented the tool to measure Web sites' quality four times(called WebQual). The WebQual 1.0, Step one invented a measuring item for information quality based on QFD, and this had been verified by students of UK business school. The Web Qual 2.0, Step two invented for interaction quality, and had been judged by customers of on-line bookshop. The WebQual 3.0, Step three invented by consolidating the WebQual 1.0 for information quality and the WebQual2.0 for interactionquality. It includes 3-quality-dimension, information quality, interaction quality, site design, and had been assessed and confirmed by auction sites(e-bay, Amazon, QXL). Furtheron, through the former empirical studies, the authors changed sites quality into usability by judging that usability is a concept how customers interact with or perceive Web sites and It is used widely for accessing Web sites. By this process, WebQual 4.0 was invented, and is consist of 3-quality-dimension; information quality, interaction quality, usability, 22 items. However, because WebQual 4.0 is focusing on technical part, it's usable at the Website's design part, on the other hand, it's not usable at the Web site's pleasant experience part. Parasuraman, Zeithaml & Malhorta(2002; 2005) had invented the measure for measuring on-line service quality in 2002 and 2005. The study in 2002 divided on-line service quality into 5 dimensions. But these were not well-organized, so there needed to be studied again totally. So Parasuraman, Zeithaml & Malhorta(2005) re-worked out the study about on-line service quality measure base on 2002's study and invented E-S-QUAL. After they invented preliminary measure for on-line service quality, they made up a question for customers who had purchased at amazon.com and walmart.com and reassessed this measure. And they perfected an invention of E-S-QUAL consists of 4 dimensions, 22 items of efficiency, system availability, fulfillment, privacy. Efficiency measures assess to sites and usability and others, system availability measures accurate technical function of sites and others, fulfillment measures promptness of delivering products and sufficient goods and others and privacy measures the degree of protection of data about their customers and so on. 2.2 Service Recovery Service industries tend to minimize the losses by coping with service failure promptly. This responses of service providers to service failure mean service recovery(Kelly & Davis, 1994). Bitner(1990) went on his study from customers' view about service providers' behavior for customers to recognize their satisfaction/dissatisfaction at service point. According to them, to manage service failure successfully, exact recognition of service problem, an apology, sufficient description about service failure and some tangible compensation are important. Parasuraman, Zeithaml & Malhorta(2005) approached the service recovery from how to measure, rather than how to manage, and moved to on-line market not to off-line, then invented E-RecS-QUAL which is a measuring tool about on-line service recovery. 2.3 Customer Satisfaction The definition of customer satisfaction can be divided into two points of view. First, they approached customer satisfaction from outcome of comsumer. Howard & Sheth(1969) defined satisfaction as 'a cognitive condition feeling being rewarded properly or improperly for their sacrifice.' and Westbrook & Reilly(1983) also defined customer satisfaction/dissatisfaction as 'a psychological reaction to the behavior pattern of shopping and purchasing, the display condition of retail store, outcome of purchased goods and service as well as whole market.' Second, they approached customer satisfaction from process. Engel & Blackwell(1982) defined satisfaction as 'an assessment of a consistency in chosen alternative proposal and their belief they had with them.' Tse & Wilton(1988) defined customer satisfaction as 'a customers' reaction to discordance between advance expectation and ex post facto outcome.' That is, this point of view that customer satisfaction is process is the important factor that comparing and assessing process what they expect and outcome of consumer. Unlike outcome-oriented approach, process-oriented approach has many advantages. As process-oriented approach deals with customers' whole expenditure experience, it checks up main process by measuring one by one each factor which is essential role at each step. And this approach enables us to check perceptual/psychological process formed customer satisfaction. Because of these advantages, now many studies are adopting this process-oriented approach(Yi, 1995). 2.4 Loyalty Intention Loyalty has been studied by dividing into behavioral approaches, attitudinal approaches and complex approaches(Dekimpe et al., 1997). In the early years of study, they defined loyalty focusing on behavioral concept, behavioral approaches regard customer loyalty as "a tendency to purchase periodically within a certain period of time at specific retail store." But the loyalty of behavioral approaches focuses on only outcome of customer behavior, so there are someone to point the limits that customers' decision-making situation or process were neglected(Enis & Paul, 1970; Raj, 1982; Lee, 2002). So the attitudinal approaches were suggested. The attitudinal approaches consider loyalty contains all the cognitive, emotional, voluntary factors(Oliver, 1997), define the customer loyalty as "friendly behaviors for specific retail stores." However these attitudinal approaches can explain that how the customer loyalty form and change, but cannot say positively whether it is moved to real purchasing in the future or not. This is a kind of shortcoming(Oh, 1995). 3. Research Design 3.1 Research Model Based on the objects of this study, the research model derived is