• Proceedings of the Safety Management and Science Conference
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• 2004.11a
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• pp.355-380
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• 2004

I test the hypothesis that the gradual diffusion of information across asset markets leads to cross-asset return predictability in Korea. Using thirty-six industry portfolios and the broad market index as our test assets, I establish several key results. First, a number of industries such as semiconductor, electronics, metal, and petroleum lead the stock market by up to one month. In contrast, the market, which is widely followed, only leads a few industries. Importantly, an industry's ability to lead the market is correlated with its propensity to forecast various indicators of economic activity such as industrial production growth. Consistent with our hypothesis, these findings indicate that the market reacts with a delay to information in industry returns about its fundamentals because information diffuses only gradually across asset markets. Traditional theories of asset pricing assume that investors have unlimited information-processing capacity. However, this assumption does not hold for many traders, even the most sophisticated ones. Many economists recognize that investors are better characterized as being only boundedly rational(see Shiller(2000), Sims(2201)). Even from casual observation, few traders can pay attention to all sources of information much less understand their impact on the prices of assets that they trade. Indeed, a large literature in psychology documents the extent to which even attention is a precious cognitive resource(see, eg., Kahneman(1973), Nisbett and Ross(1980), Fiske and Taylor(1991)). A number of papers have explored the implications of limited information- processing capacity for asset prices. I will review this literature in Section II. For instance, Merton(1987) develops a static model of multiple stocks in which investors only have information about a limited number of stocks and only trade those that they have information about. Related models of limited market participation include brennan(1975) and Allen and Gale(1994). As a result, stocks that are less recognized by investors have a smaller investor base(neglected stocks) and trade at a greater discount because of limited risk sharing. More recently, Hong and Stein(1999) develop a dynamic model of a single asset in which information gradually diffuses across the investment public and investors are unable to perform the rational expectations trick of extracting information from prices. Hong and Stein(1999). My hypothesis is that the gradual diffusion of information across asset markets leads to cross-asset return predictability. This hypothesis relies on two key assumptions. The first is that valuable information that originates in one asset reaches investors in other markets only with a lag, i.e. news travels slowly across markets. The second assumption is that because of limited information-processing capacity, many (though not necessarily all) investors may not pay attention or be able to extract the information from the asset prices of markets that they do not participate in. These two assumptions taken together leads to cross-asset return predictability. My hypothesis would appear to be a very plausible one for a few reasons. To begin with, as pointed out by Merton(1987) and the subsequent literature on segmented markets and limited market participation, few investors trade all assets. Put another way, limited participation is a pervasive feature of financial markets. Indeed, even among equity money managers, there is specialization along industries such as sector or market timing funds. Some reasons for this limited market participation include tax, regulatory or liquidity constraints. More plausibly, investors have to specialize because they have their hands full trying to understand the markets that they do participate in

• Tuberculosis and Respiratory Diseases
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• v.40 no.2
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• pp.135-146
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• 1993

Background: In acute pulmonary embolism it has been postulated that the constriction of bronchi and pulmonary artery secondary to neurohumoral response plays an important role in cardiopulmonary dysfunction in addition to the mechanical obstruction of pulmonary artery. Serotonin is considered as the most important mediator. Positive end expiratory pressure (PEEP) stimulates $PGI_2$ secretion from the vascular endothelium, but its role in acute pulmonary embolism is still in controversy. Methods: To study the cardiopulmonary effect and therapeutic role of Ketanserin, selective antagonist of 5-HT2 receptor, and PEEP in acute pulmonary embolism experimental acute pulmonary embolism was induced in dogs with autologous blood clot. The experimental animals were divided into 3 groups, that is control group, Ketanserin injection group and PEEP application group. Results: Thirty minutes after embolization, mean pulmonary arterial pressure and pulmonary vascular resistance increased and cardiac output decreased. $PaO_2,\;P\bar{v}O_2$ and oxygen transport decreased and physiological shunt and $PaCO_2$ increased. After injection of Ketanserin, comparing with control group, mean pulmonary arterial pressure, pulmonary vascular resistance and physiological shunt decreased, while cardiac output, $PaO_2$ and oxygen transport increased. All these changes sustained till 4 hours after embolization. After PEEP application pulmonary vascular resistance, $PaO_2$ and $PaCO_2$ increased, while physiological shunt, cardiac output and oxygen transport decreased. After discontinuation of PEEP, mean pulmonary arterial pressure and pulmonary vascular resistance decreased and were lower than control group, while $PaO_2$ and cardiac output increased and higher than control group. $PaCO_2$ decreased but showed no significant difference comparing with control group. Conclusion: It can be concluded that Ketanserin is effective for the treatment of acute pulmonary embolism. With PEEP hemodynamic status deteriorated, but improved better than control group after discontinuation of PEEP. Thus PEEP may be applied carefully for short period in acute pulmonary embolism if the hemodynamic status is tolerable.

• Tuberculosis and Respiratory Diseases
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• v.48 no.1
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• pp.14-23
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• 2000

Background: The evaluation of candidates for successful lung resection is important. Our study was conducted to determine the preoperative predictors of postoperative mortality and morbidity in lung cancer patients with impaired lung function. Method; Between October 1, 1995 and August 31, 1997, 36 lung resection candidates for lung cancer with $FEV_1$ of less than 2L or 60% of predicted value were included prospectively. Age, sex, weight loss, hematocrit, serum albumin, EKG and concomitant illness were considered as systemic potential predictors for successful lung resection. Smoking history, presence of pneumonia, dyspnea scale(l to 4), arterial blood gas analysis with room air breathing, routine pulmonary function test were also included for the analysis. In addition, predicted postoperative(ppo) pulmonary factors such as ppo-$FEV_1$ ppo-diffusing capacity(DLco), predicted postoperative product(PPP) of ppo-$FEV_1%{\times}$ppo-DLco% and ppo-maximal $O_2$ uptake($VO_2$max) were also measured. Results: There were 31 men and 5 women with the median age of 65 years(range, 44 to 82) and a mean $FEV_1$ of $1.78{\pm}0.06L$. Pneumonectomy was performed in 14 patients, bilobectomy in 8, lobectomy in 14. Pulmonary complications developed in 10 patients; cardiac complications in 3, other complications(empyema, air leak, bleeding) in 4. Twelve patients were managed in the intensive care unit for more than 48 hours. Two patients died within 30 days after operation. The ppo-$VO_2$max was less than 10 ml/kg/min in these two patients. MVV was the only predictor for the pulmonary complications. However, there was no predictor for the post operative death in this study. Conclusions: Based on the results, MVV was the useful predictor for postoperative pulmonary complications in lung cancer resection candidates with impaired lung function In addition, ppo-$VO_2$max value less than 10 ml/kg/min was associated with postoperative death, so exercise pulmonary function test could be useful as preoperative test. But further studies are needed to validate this result.

• Journal of the korean academy of Pediatric Dentistry
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• v.28 no.4
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• pp.709-727
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• 2001

The pattern of programmed cell death(PCD) has been examined during the early developmental period of development in mouse embryos, from embryonic day 4.5(E4.5) to E11.5 Embryos from Balb/c breedings were harvested at various embryonic stages between E4.5 and El1.5. Cell death was analysed by in situ terminal deoxynucleotidyl transferase mediated dUTP nick end labeling(TUNEL) staining in tissue sections and whole embryos. At the blastocyst stage(E4.5), a very few apoptotic cells were found in the inner cell mass of the blastocyst. In the early egg cylinder stage(35.0-5.5), a few apoptotic cells were detected in the embryonic ectoderm, the embryonic endoerm and the proamniotic cavity. In the advanced egg cylinder stage(E5.5-6.5), TUNEL-posifive cells were observed in the extra-embryonic ectoderm and extra-embryonic endoderm as well as in the embryonic ectoderm, embryonic visceral endoderm and proamniotic cavity. In the streak stage(E6.75-7.75), many TUNEL-positive cells were found in the ectoplacental cone. In contrast, only very few apoptotic cells were found in the chorion and extra-embryonic endoderm in extra-embryonic regions. In intra-embryonic region, a few apoptotic cells were randomly found in the embryonic ectoderm, mesoderm and visceral endoderm. At the early somitogenesis stage(E8.0-8.5), most apoptotic cells were observed in the most cranial portion of neural fold (neural ectoderm and adjacent ectoderm). At the mid somitogenesis stage(39.0-9.5), the otic placode first showed TUNEL-positive at this stage. Small number of TUNEL-positive cells were also first seen around optic placode and branchial arches. Three streams of TUNEL-positive cells were clearly seen in the cranial region at 59.5-9.75. At E10.5, apoptotic cells were localized in the developing eye, the junctional portion of medial nasal, lateral nasal and maxillary processes, the lateral portion of branchial arches, the junction of bilateral mandibular processes, and apical ectodermal ridges of limb buds. At E11.5, apoptotic cells were noticeably decreased in most area, except the developing limbs and several somites in the tail region. In this study, the global temporospatial pattern of PCD throughout early development of mouse embryos was discussed. It may provide the basis for further studies on its role in the morphogenesis of the embryo.

• Journal of Preventive Medicine and Public Health
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• v.30 no.2 s.57
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• pp.381-391
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• 1997

In an epidemiological study of metal workers exposed to metalworking fluids (MWF), the prevalence time of Evolution, seasonal occurrence and clinical type of contact dermatitis were investigated. Compostional analysis of MWF with HPLC, dermatological examination and two consecutive questionnaire surveys were conducted. Study population was divided into two groups ; workers contact to cutting oil and workers contact to rust preventive oil. In the analysis of MWF, aliphatic hydrocarbons, having 12-20 carbons, was most common composition(49.04%) of cutting oil otherwise, major contents (90.99%) of the rust preventives oil were aliphatic hydrocarbons composed of 6-9 carbons. The frequency (point prevalence) of contact dermatitis(CD) was 7(12.7 per 100 subjects) in the dermatological examination of 55 workers. As the result of second survey for contact dermatitis, cumulative prevalence of oil working full-time and recent 1 year prevalence in two groups were 28.0, 16.7 and 15.1, 12.5 per 100 subjects. There were no difference in the prevalence of CD by oil, age, oil contact duration. Summer is the most common evolution season in workers exposed to cutting oil, but not in workers exposed to rust preventive oil. Major clinical type of CD was erythematous papules in both groups. It presents the importance of preventive measures that 51.1% suffer from contact dermatitis had medical care at their own expense, and 47.1% of them felt serious about their contact dermatitis. From the fact that 68.6% think cotton gloves protective apparatus, we emphasize the need for health education.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.19-43
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• 2014

The Pareto principle, also known as the 80-20 rule, states that roughly 80% of the effects come from 20% of the causes for many events including natural phenomena. It has been recognized as a golden rule in business with a wide application of such discovery like 20 percent of customers resulting in 80 percent of total sales. On the other hand, the Long Tail theory, pointing out that "the trivial many" produces more value than "the vital few," has gained popularity in recent times with a tremendous reduction of distribution and inventory costs through the development of ICT(Information and Communication Technology). This study started with a view to illuminating how these two primary business paradigms-Pareto principle and Long Tail theory-relates to the success of virtual knowledge collaboration. The importance of virtual knowledge collaboration is soaring in this era of globalization and virtualization transcending geographical and temporal constraints. Many previous studies on knowledge sharing have focused on the factors to affect knowledge sharing, seeking to boost individual knowledge sharing and resolve the social dilemma caused from the fact that rational individuals are likely to rather consume than contribute knowledge. Knowledge collaboration can be defined as the creation of knowledge by not only sharing knowledge, but also by transforming and integrating such knowledge. In this perspective of knowledge collaboration, the relative distribution of knowledge sharing among participants can count as much as the absolute amounts of individual knowledge sharing. In particular, whether the more contribution of the upper 20 percent of participants in knowledge sharing will enhance the efficiency of overall knowledge collaboration is an issue of interest. This study deals with the effect of this sort of knowledge sharing distribution on the efficiency of knowledge collaboration and is extended to reflect the work characteristics. All analyses were conducted based on actual data instead of self-reported questionnaire surveys. More specifically, we analyzed the collaborative behaviors of editors of 2,978 English Wikipedia featured articles, which are the best quality grade of articles in English Wikipedia. We adopted Pareto ratio, the ratio of the number of knowledge contribution of the upper 20 percent of participants to the total number of knowledge contribution made by the total participants of an article group, to examine the effect of Pareto principle. In addition, Gini coefficient, which represents the inequality of income among a group of people, was applied to reveal the effect of inequality of knowledge contribution. Hypotheses were set up based on the assumption that the higher ratio of knowledge contribution by more highly motivated participants will lead to the higher collaboration efficiency, but if the ratio gets too high, the collaboration efficiency will be exacerbated because overall informational diversity is threatened and knowledge contribution of less motivated participants is intimidated. Cox regression models were formulated for each of the focal variables-Pareto ratio and Gini coefficient-with seven control variables such as the number of editors involved in an article, the average time length between successive edits of an article, the number of sections a featured article has, etc. The dependent variable of the Cox models is the time spent from article initiation to promotion to the featured article level, indicating the efficiency of knowledge collaboration. To examine whether the effects of the focal variables vary depending on the characteristics of a group task, we classified 2,978 featured articles into two categories: Academic and Non-academic. Academic articles refer to at least one paper published at an SCI, SSCI, A&HCI, or SCIE journal. We assumed that academic articles are more complex, entail more information processing and problem solving, and thus require more skill variety and expertise. The analysis results indicate the followings; First, Pareto ratio and inequality of knowledge sharing relates in a curvilinear fashion to the collaboration efficiency in an online community, promoting it to an optimal point and undermining it thereafter. Second, the curvilinear effect of Pareto ratio and inequality of knowledge sharing on the collaboration efficiency is more sensitive with a more academic task in an online community.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.1-27
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• 2017

It is known that the economic sentiment index and macroeconomic indicators are closely related because economic agent's judgment and forecast of the business conditions affect economic fluctuations. For this reason, consumer sentiment or confidence provides steady fodder for business and is treated as an important piece of economic information. In Korea, private consumption accounts and consumer sentiment index highly relevant for both, which is a very important economic indicator for evaluating and forecasting the domestic economic situation. However, despite offering relevant insights into private consumption and GDP, the traditional approach to measuring the consumer confidence based on the survey has several limits. One possible weakness is that it takes considerable time to research, collect, and aggregate the data. If certain urgent issues arise, timely information will not be announced until the end of each month. In addition, the survey only contains information derived from questionnaire items, which means it can be difficult to catch up to the direct effects of newly arising issues. The survey also faces potential declines in response rates and erroneous responses. Therefore, it is necessary to find a way to complement it. For this purpose, we construct and assess an index designed to measure consumer economic sentiment index using sentiment analysis. Unlike the survey-based measures, our index relies on textual analysis to extract sentiment from economic and financial news articles. In particular, text data such as news articles and SNS are timely and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. There exist two main approaches to the automatic extraction of sentiment from a text, we apply the lexicon-based approach, using sentiment lexicon dictionaries of words annotated with the semantic orientations. In creating the sentiment lexicon dictionaries, we enter the semantic orientation of individual words manually, though we do not attempt a full linguistic analysis (one that involves analysis of word senses or argument structure); this is the limitation of our research and further work in that direction remains possible. In this study, we generate a time series index of economic sentiment in the news. The construction of the index consists of three broad steps: (1) Collecting a large corpus of economic news articles on the web, (2) Applying lexicon-based methods for sentiment analysis of each article to score the article in terms of sentiment orientation (positive, negative and neutral), and (3) Constructing an economic sentiment index of consumers by aggregating monthly time series for each sentiment word. In line with existing scholarly assessments of the relationship between the consumer confidence index and macroeconomic indicators, any new index should be assessed for its usefulness. We examine the new index's usefulness by comparing other economic indicators to the CSI. To check the usefulness of the newly index based on sentiment analysis, trend and cross - correlation analysis are carried out to analyze the relations and lagged structure. Finally, we analyze the forecasting power using the one step ahead of out of sample prediction. As a result, the news sentiment index correlates strongly with related contemporaneous key indicators in almost all experiments. We also find that news sentiment shocks predict future economic activity in most cases. In almost all experiments, the news sentiment index strongly correlates with related contemporaneous key indicators. Furthermore, in most cases, news sentiment shocks predict future economic activity; in head-to-head comparisons, the news sentiment measures outperform survey-based sentiment index as CSI. Policy makers want to understand consumer or public opinions about existing or proposed policies. Such opinions enable relevant government decision-makers to respond quickly to monitor various web media, SNS, or news articles. Textual data, such as news articles and social networks (Twitter, Facebook and blogs) are generated at high-speeds and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. Although research using unstructured data in economic analysis is in its early stages, but the utilization of data is expected to greatly increase once its usefulness is confirmed.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.1-17
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• 2015

In this paper, we report what we have observed with regards to a prediction model for the military based on enlisted men's internal(cumulative records) and external data(SNS data). This work is significant in the military's efforts to supervise them. In spite of their effort, many commanders have failed to prevent accidents by their subordinates. One of the important duties of officers' work is to take care of their subordinates in prevention unexpected accidents. However, it is hard to prevent accidents so we must attempt to determine a proper method. Our motivation for presenting this paper is to mate it possible to predict accidents using enlisted men's internal and external data. The biggest issue facing the military is the occurrence of accidents by enlisted men related to maladjustment and the relaxation of military discipline. The core method of preventing accidents by soldiers is to identify problems and manage them quickly. Commanders predict accidents by interviewing their soldiers and observing their surroundings. It requires considerable time and effort and results in a significant difference depending on the capabilities of the commanders. In this paper, we seek to predict accidents with objective data which can easily be obtained. Recently, records of enlisted men as well as SNS communication between commanders and soldiers, make it possible to predict and prevent accidents. This paper concerns the application of data mining to identify their interests, predict accidents and make use of internal and external data (SNS). We propose both a topic analysis and decision tree method. The study is conducted in two steps. First, topic analysis is conducted through the SNS of enlisted men. Second, the decision tree method is used to analyze the internal data with the results of the first analysis. The dependent variable for these analysis is the presence of any accidents. In order to analyze their SNS, we require tools such as text mining and topic analysis. We used SAS Enterprise Miner 12.1, which provides a text miner module. Our approach for finding their interests is composed of three main phases; collecting, topic analysis, and converting topic analysis results into points for using independent variables. In the first phase, we collect enlisted men's SNS data by commender's ID. After gathering unstructured SNS data, the topic analysis phase extracts issues from them. For simplicity, 5 topics(vacation, friends, stress, training, and sports) are extracted from 20,000 articles. In the third phase, using these 5 topics, we quantify them as personal points. After quantifying their topic, we include these results in independent variables which are composed of 15 internal data sets. Then, we make two decision trees. The first tree is composed of their internal data only. The second tree is composed of their external data(SNS) as well as their internal data. After that, we compare the results of misclassification from SAS E-miner. The first model's misclassification is 12.1%. On the other hand, second model's misclassification is 7.8%. This method predicts accidents with an accuracy of approximately 92%. The gap of the two models is 4.3%. Finally, we test if the difference between them is meaningful or not, using the McNemar test. The result of test is considered relevant.(p-value : 0.0003) This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of enlisted men's data. Additionally, various independent variables used in the decision tree model are used as categorical variables instead of continuous variables. So it suffers a loss of information. In spite of extensive efforts to provide prediction models for the military, commanders' predictions are accurate only when they have sufficient data about their subordinates. Our proposed methodology can provide support to decision-making in the military. This study is expected to contribute to the prevention of accidents in the military based on scientific analysis of enlisted men and proper management of them.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.125-140
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• 2013

We call a data set in which the number of records belonging to a certain class far outnumbers the number of records belonging to the other class, 'imbalanced data set'. Most of the classification techniques perform poorly on imbalanced data sets. When we evaluate the performance of a certain classification technique, we need to measure not only 'accuracy' but also 'sensitivity' and 'specificity'. In a customer churn prediction problem, 'retention' records account for the majority class, and 'churn' records account for the minority class. Sensitivity measures the proportion of actual retentions which are correctly identified as such. Specificity measures the proportion of churns which are correctly identified as such. The poor performance of the classification techniques on imbalanced data sets is due to the low value of specificity. Many previous researches on imbalanced data sets employed 'oversampling' technique where members of the minority class are sampled more than those of the majority class in order to make a relatively balanced data set. When a classification model is constructed using this oversampled balanced data set, specificity can be improved but sensitivity will be decreased. In this research, we developed a hybrid model of support vector machine (SVM), artificial neural network (ANN) and decision tree, that improves specificity while maintaining sensitivity. We named this hybrid model 'hybrid SVM model.' The process of construction and prediction of our hybrid SVM model is as follows. By oversampling from the original imbalanced data set, a balanced data set is prepared. SVM_I model and ANN_I model are constructed using the imbalanced data set, and SVM_B model is constructed using the balanced data set. SVM_I model is superior in sensitivity and SVM_B model is superior in specificity. For a record on which both SVM_I model and SVM_B model make the same prediction, that prediction becomes the final solution. If they make different prediction, the final solution is determined by the discrimination rules obtained by ANN and decision tree. For a record on which SVM_I model and SVM_B model make different predictions, a decision tree model is constructed using ANN_I output value as input and actual retention or churn as target. We obtained the following two discrimination rules: 'IF ANN_I output value <0.285, THEN Final Solution = Retention' and 'IF ANN_I output value ${\geq}0.285$, THEN Final Solution = Churn.' The threshold 0.285 is the value optimized for the data used in this research. The result we present in this research is the structure or framework of our hybrid SVM model, not a specific threshold value such as 0.285. Therefore, the threshold value in the above discrimination rules can be changed to any value depending on the data. In order to evaluate the performance of our hybrid SVM model, we used the 'churn data set' in UCI Machine Learning Repository, that consists of 85% retention customers and 15% churn customers. Accuracy of the hybrid SVM model is 91.08% that is better than that of SVM_I model or SVM_B model. The points worth noticing here are its sensitivity, 95.02%, and specificity, 69.24%. The sensitivity of SVM_I model is 94.65%, and the specificity of SVM_B model is 67.00%. Therefore the hybrid SVM model developed in this research improves the specificity of SVM_B model while maintaining the sensitivity of SVM_I model.