• 지능정보연구
• /
• 제7권2호
• /
• pp.83-93
• /
• 2001

기업부실예측은 과거로부터 많은 연구가 이루어진 분야로, 주로 통계기법에 의한 분류예측문제로 다루어져 왔다. 최근에는 인공신경망, 의사결정나무 등 비선형성을 반영할 수 있는 인공지능 기법을 적용한 연구가 많이 수행되고 있다. 본 연구에서는 최적화에 주로 활용하는 인공지능 기법인 유전자 알고리즘을 규칙추출을 통한 기업부실예측 모형의 개발에 적용하고, 활용가능성을 검증하였다.

• 한국경영과학회지
• /
• 제26권4호
• /
• pp.55-70
• /
• 2001

Artificial neural network(ANN) models have been widely used for the classification problems in business such as bankruptcy prediction, credit evaluation, etc. Although the application of ANN to classification of consumers＇ choice behavior is a promising research area, there have been only a few researches. In general, most of the researches have reported that the classification performance of the ANN models were better than conventional statistical model Because the survey data on consumer behavior may include much noise and missing data, ANN model will be more robust than conventional statistical models welch need various assumptions. The purpose of this paper is to study the potential of the ANN model for forecasting consumers＇ choice behavior based on survey data. The data was collected by questionnaires to the shoppers of department stores and discount stores. Then the correct classification rates of the ANN models for the training and test sample with that of multiple discriminant analysis(MDA) and logistic regression(Logit) model. The performance of the ANN models were betted than the performance of the MDA and Logit model with respect to correct classification rate. By using input variables identified as significant in the stepwise MDA, the performance of the ANN models were improved.

• 한국정보시스템학회지:정보시스템연구
• /
• 제27권3호
• /
• pp.63-80
• /
• 2018

Purpose The purpose of this study is to build a machine learning-based customer classification model to promote customer expansion effect of the free sample promotion. Specifically, the proposed model classifies potential target customers who are expected to purchase the products included in the free sample promotion after receiving the free samples. Design/methodology/approach This study proposes to build a customer classification model for determining customers suitable for providing free samples by using various machine learning techniques such as logistic regression, multiple discriminant analysis, case-based reasoning, decision tree, artificial neural network, and support vector machine. To validate the usefulness of the proposed model, we apply it to a real-world free sample-based target marketing case of a Korean major cosmetic retail company. Findings Experimental results show that a machine learning-based customer classification model presents satisfactory accuracy ranging from 70% to 75%. In particular, support vector machine is found to be the most effective machine learning technique for free sample-based target marketing model. Our study sheds a light on customer relationship management strategies using free sample promotions.

• 한국지능정보시스템학회:학술대회논문집
• /
• 한국지능정보시스템학회 2005년도 공동추계학술대회
• /
• pp.516-525
• /
• 2005

Case-based reasoning (CBR) has been widely used in various areas due to its convenience and strength in complex problem solving. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. Most prior studies have tried to optimize the weights of the features or selection process of appropriate instances. But, these approaches have been performed independently until now. Simultaneous optimization of these components may lead to better performance than in naive models. In particular, there have been few attempts to simultaneously optimize the weight of the features and selection of the instances for CBR. Here we suggest a simultaneous optimization model of these components using a genetic algorithm (GA). We apply it to a customer classification model which utilizes demographic characteristics of customers as inputs to predict their buying behavior for a specific product. Experimental results show that simultaneously optimized CBR may improve the classification accuracy and outperform various optimized models of CBR as well as other classification models including logistic regression, multiple discriminant analysis, artificial neural networks and support vector machines.

• 대한치과교정학회지
• /
• 제39권2호
• /
• pp.112-119
• /
• 2009

본 연구는 다변량 기법을 도입하여 치아 크기의 다양성을 고려하면서 정확성이 높은 혼합치 열기 분석법을 개발하기 위해 시행되었다. 견치 및 소구치 크기를 예측하는 데 이용된 변수는 상악 중절치, 상악 제1대구치, 하악 중절치, 하악 측절치 및 하악 제1대구치로서 총 5개 치아 크기 변수가 이용되었다. 우선 정상교합자 연구 표본 307명을 5개 치아 변수를 이용하여 k-means 군집 분석으로 치아 크기에 따라 나눈 후 판별식을 이용, 치아 크기가 큰 그룹과 작은 그룹으로 분류하였다. 이후 견치와 소구치 크기의 합을 예측하기 위하여 남녀별, 상하악별, 치아 크기 그룹별로 다중선형 분석을 이용하여 회귀식을 구했다. 검증 표본에는 504명의 부정교합자가 이용되었으며, 이들에 대하여 정상교합자로부터 도출된 판별식을 이용하여 2그룹으로 할당한 후 정상교합자로부터 도출된 회귀식을 이용하여 상악과 하악의 견치 및 소구치 크기 합을 예측하였다. 오차 분석 결과 정상교합자는 최대 0.71, 부정교합자 검증표본은 최대 0.82 mm의 residual standard deviation 값을 보였다. 부정교합 분류별, 치아 크기 패턴별로 예측 오차의 유의한 차이는 없었다. 1 mm 및 2 mm 이상의 예측 오차를 보인 빈도는 각각 17.3%와 1.8%였다. 본 연구 결과 도출된 혼합치열기 분석법은 기존의 연구들과 비교하여 그 정확성이 높은 것으로 고찰되었다. 다만, 임상 적용 시 복잡한 계산 과정으로 인하여 전산화 환경에서 더욱 유용할 것으로 생각된다.

• 한국산학기술학회논문지
• /
• 제12권3호
• /
• pp.1128-1135
• /
• 2011

본 연구는 육상 투척 선수들의 자기관리와 심리적 행복감의 관계를 실증적으로 규명하기 위한 것으로 연구의 대상자는 대한육상경기연맹에 등록된 고등부, 대학, 일반실업 육상투척선수로서 제91회 전국체전 대회에 출전한 고등부, 대학, 일반실업 선수(남:82, 여:102)로서 총 184명을 대상으로 조사하였다. 수집된 자료를 분석하기 위해 변량분석(ANOVA)과 중다회귀분석(multiple regression)을 실시한 결과 다음과 같은 결론을 얻었다. 첫째, 투척선수들의 사회인구학적 특성에 따른 자기관리 및 심리적 행복감에는 차이가 없는 것으로 나타났다. 둘째, 투척종목 선수들의 자기관리와 심리적 행복감에는 유의한 인과관계가 있는 것으로 나타났다. 즉, 자기관리는 심리적 행복감에 유의미한 영향을 미치는 것으로 나타났다. 이상의 결과을 종합해 볼 때, 이 연구에서는 육상경기 중 투척경기의 선수들의 사회 인구학적 특성에 따른 자기관리와 심리적 행복감에는 차이가 없고, 자기관리는 심리적 행복감에 긍정적인 영향을 미치는 것으로 나타났다. 따라서 선수들의 심리적 행복감을 통한 최적의 경기수행을 이끌어 내기 위해 철저한 자기관리를 위한 프로그램을 제공해야 한다는 종합적인 결론을 제시할 수 있다.

• 한국산림과학회지
• /
• 제105권2호
• /
• pp.157-166
• /
• 2016

덕유산 백암봉 일대의 천연림을 대상으로 산림형을 구분하고, 각 산림형별 생태적 특성을 파악하여 천이경향을 제시하였다. 사분각법을 이용하여 225개의 표본점에서 식생자료를 수집하였으며, 다양한 다변량 통계분석(Cluster분석, 지표종분석, 다중판별분석 등)을 실시하여 산림형을 구분하였다. 그 결과, 연구대상지는 5개의 산림형으로 분류되었고, 상층의 우점비율 및 입지환경에 따라 능선부에서는 신갈나무림, 계곡부에서는 들메나무-물푸레나무-층층나무림과 들메나무림, 사면하부에서는 졸참나무-소나무-신갈나무림, 소나무림 등이 분포하여 입지조건에 따라 수종구성 차이가 뚜렷한 것으로 나타났다. 산림유형별 생태적, 환경적 특성을 근거로 천이경향을 추정한 결과, 현재의 산림형은 신갈나무림, 들메나무림, 중생혼합림, 참나무-서어나무림 등으로 천이가 진행될 것으로 예상되었다.

• 기본간호학회지
• /
• 제8권1호
• /
• pp.69-80
• /
• 2001

This study was done to investigate the relation of smoking temptation, stage of change in smoking cessation, nicotine dependency and perceived health status in middle aged men. Convenience samples of 176 subjects who were either smoked or used to smoke, aged between 30 to 64, living in Seoul and Kyungi province area in Korea were selected for the study. The data was collected from December 1, 1999 to June 30, 2000. The research instrument were Stage of Change of Smoking Cessation Measure (DiClemente et al. 1991). Smoking Temptation Measure (Velicer, DiClemente, Rossi, Prochaska. 1990), Perceived Health Status Measure (McDowell & Newell, 1996), and Nicotine Dependency Scale (FTQ: Fagerstrom, 1978). The data were analyzed using the SAS Program. The result of the study are as follows : 1. The analysis of variance and multiple comparison showed that according to the stage of change, there were significant mean differences in the three sub-factors of smoking temptation; 'positive affect situation (F=12.64, p=.0001)', 'negative affect situation (F=16.01, p=.0001)', 'habitual craving situation (F=14.43, p=.0001)' and nicotine dependency (F=4.12, p=.0033) The mean score for smoking temptation for the subjects who were in the precontemplation stage outweighed the mean score for smoking temptation for subjects who were in the maintenance stage. 2. Through discriminant analysis, it was found that negative affect situation was the most influential variable of the smoking temptation sub-factors which can be used to discriminate stage of change. 3. The analysis of Pearson correlation coefficients showed that there was a significant positive relation between nicotine dependency and negative affect situation of smoking cessation((r=0.2182, p=0.0045) and a significant negative relation between nicotine dependency and perceived health status(r=-0.2115, p=0.0059).

• Journal of Ginseng Research
• /
• 제46권2호
• /
• pp.290-295
• /
• 2022

Background: Dried and red ginseng are well-known types of processed ginseng and are widely used as healthy food. The dried and red ginseng quality may vary with the storage period of raw ginseng. Therefore, herein, the effect of the storage period of fresh ginseng on processed ginseng quality was evaluated through multicomponent quantification with statistical analysis. Methods: A method based on ultrahigh performance liquid chromatography coupled to triple quadrupole mass spectrometry in multiple-reaction monitoring mode (UPLC-MRM-MS) was developed for quantitation of ginsenosides and oligosaccharides in dried and red ginseng. Principal component analysis and partial least squares discriminant analysis were conducted to evaluate the dynamic distributions of ginsenosides and oligosaccharides after different storage periods. Results: Eighteen PPD, PPT and OLE ginsenosides and nine reducing and nonreducing oligosaccharides were identified and quantified. With storage period extension, the ginsenoside content in the processed ginseng increased slightly in the first 2 weeks and decreased gradually in the following 9 weeks. The content of reducing oligosaccharides decreased continuously as storage time extending, while that of the nonreducing oligosaccharides increased. Chemical conversions occurred during storage, based on which potential chemical markers for the storage period evaluation of fresh ginseng were screened. Conclusion: According to ginsenoside and oligosaccharide distributions, it was found that the optimal storage period was 2 weeks and that the storage period of fresh ginseng should not exceed 4 weeks at 0 ℃. This study provides deep insights into the quality control of processed ginseng and comprehensive factors for storage of raw ginseng.

• 임상간호연구
• /
• 제27권3호
• /
• pp.279-293
• /
• 2021

Purpose: The purpose of this study was to develop a Korean nursing work environment scale for critical care nurses (KNWES-CCN) and verify its validity and reliability. Methods: A total of 46 preliminary items were selected using content validity analysis of experts on 64 candidate items derived through literature reviews and in-depth interviews with critical care nurses. 535 critical care nurses from 21 hospitals responded to the preliminary questionnaire from February to March 2021. The collected data were analysed using construct, convergent and discriminant validities, and internal consistency and test-retest reliability. Results: The 23 items in 4 factors accounted for 55.6% of the total variance were identified through item analysis and exploratory factor analysis (EFA). EFA was performed with maximum likelihood method including direct oblimin method. In the confirmatory factor analysis, KNWES-CCN consisted of 21 items in 4 factors by deleting the items that were not meet the condition that the factor loading over .50 or the squared multiple correlation over .30. This model was considered to be suitable because it satisfied the fit index and acceptable criteria of the model [𝒳²=440.47 (p<.001), CMIN/DF=2.41, GFI=.86, SRMR=.06, RMSEA=.07, TLI=.90, CFI=.91]. The item total correlation values ranged form .32 to .73 and its internal consistency was Cronbach's α=.92. The reliability of the test-retest correlation coefficient was .72 and the intra-class correlation coefficient was .83. Conclusion: The KNWES-CCN showed good validity and reliability. Therefore, it is expected that the use of this scale would measure and improve nursing work environment for critical care nurses in Korea.