• 대한인간공학회지
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• 제21권1호
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• pp.1-14
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• 2002

The main purpose of this paper is to present selection criteria for ROK Airforce pilot training candidates in order to save costs involved in sequential pilot training. We use classification models such Decision Tree, Logistic Regression and Neural Network based on aptitude test results of 288 ROK Air Force applicants in 1994-1996. Different models are compared in terms of classification accuracy, ROC and Lift-value. Neural network is evaluated as the best model for each sequential flight test result while Logistic regression model outperforms the rest of them for discriminating the last flight test result. Therefore we suggest a pilot selection criterion based on this logistic regression. Overall. we find that the factors such as Attention Sharing, Speed Tracking, Machine Comprehension and Instrument Reading Ability having significant effects on the flight results. We expect that the use of our criteria can increase the effectiveness of flight resources.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.17-28
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• 2023

Today Agriculture segment is a significant supporter of Indian economy as it represents 18% of India's Gross Domestic Product (GDP) and it gives work to half of the nation's work power. Farming segment are required to satisfy the expanding need of food because of increasing populace. Therefore, to cater the ever-increasing needs of people of nation yield prediction is done at prior. The farmers are also benefited from yield prediction as it will assist the farmers to predict the yield of crop prior to cultivating. There are various parameters that affect the yield of crop like rainfall, temperature, fertilizers, ph level and other atmospheric conditions. Thus, considering these factors the yield of crop is thus hard to predict and becomes a challenging task. Thus, motivated this work as in this work dataset of different states producing different crops in different seasons is prepared; which was further pre-processed and there after machine learning techniques Gradient Boosting Regressor, Random Forest Regressor, Decision Tree Regressor, Ridge Regression, Polynomial Regression, Linear Regression are applied and their results are compared using python programming.

• International Journal of Computer Science & Network Security
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• 제21권7호
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• pp.350-358
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• 2021

In modern years, the performance of the students is analysed with lot of difficulties, which is a very important problem in all the academic institutions. The main idea of this paper is to analyze and evaluate the academic performance of the college students with bipolar disorder by applying data mining classification algorithms using Jupiter Notebook, python tool. This tool has been generally used as a decision-making tool in terms of academic performance of the students. The various classifiers could be logistic regression, random forest classifier gini, random forest classifier entropy, decision tree classifier, K-Neighbours classifier, Ada Boost classifier, Extra Tree Classifier, GaussianNB, BernoulliNB are used. The results of such classification model deals with 13 measures like Accuracy, Precision, Recall, F1 Measure, Sensitivity, Specificity, R Squared, Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, TPR, TNR, FPR and FNR. Therefore, conclusion could be reached that the Decision Tree Classifier is better than that of different algorithms.

• 한국기술혁신학회:학술대회논문집
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• 한국기술혁신학회 2016년도 추계학술대회 논문집
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• pp.70-76
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• 2016

• 지질공학
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• 제29권4호
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• pp.417-425
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• 2019

농업용 지하수 이용은 국내 지하수 이용량의 51.8%를 차지하고 있어 정확한 이용량 산정은 지하수의 효율적 정책 추진을 위하여 중요하다. 본 연구에서는 370개 관정의 이용량 실측 자료를 토대로 회귀나무 모형을 적용한 농업용(전작 및 답작용) 관정의 지하수 이용량의 산정 기법을 개발하고자 하였다. 모델의 입력 변수는 우물의 심도, 토출관 구경, 양수능력 등 3개가 유의한 것으로 평가되었으며, 모델에서의 각 변수의 중요도는 우물의 심도가 75%, 토출관 구경이 17%, 양수능력이 8%로 나타났다. 회귀나무 모형에 의한 농업용(전작 및 답작용) 관정의 일 이용량은 실측 이용량과 매우 유사한 것으로 평가되었으며, 기존의 국토교통부에서 제시한 추정식에 비하여 실측 이용량에 보다 근사한 것으로 나타났다. 향후 추가적인 실측 표본 자료를 확보하여 본 방법을 보완, 적용한다면 지하수 이용량 통계의 신뢰도가 크게 향상될 것으로 기대된다.

• Korean Journal of Acupuncture
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• 제33권3호
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• pp.102-113
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• 2016

Objectives : This study is aimed at developing and discussing the prediction model of blood stasis pattern of traditional Korean medicine(TKM) using machine learning algorithms: multiple logistic regression and decision tree model. Methods : First, we reviewed the blood stasis(BS) questionnaires of Korean, Chinese, and Japanese version to make a integrated BS questionnaire of patient-reported outcomes. Through a human subject research, patients-reported BS symptoms data were acquired. Next, experts decisions of 5 Korean medicine doctor were also acquired, and supervised learning models were developed using multiple logistic regression and decision tree. Results : Integrated BS questionnaire with 24 items was developed. Multiple logistic regression models with accuracy of 0.92(male) and 0.95(female) validated by 10-folds cross-validation were constructed. By decision tree modeling methods, male model with 8 decision node and female model with 6 decision node were made. In the both models, symptoms of 'recent physical trauma', 'chest pain', 'numbness', and 'menstrual disorder(female only)' were considered as important factors. Conclusions : Because machine learning, especially supervised learning, can reveal and suggest important or essential factors among the very various symptoms making up a pattern identification, it can be a very useful tool in researching diagnostics of TKM. With a proper patient-reported outcomes or well-structured database, it can also be applied to a pre-screening solutions of healthcare system in Mibyoung stage.

• 응용통계연구
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• 제24권3호
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• pp.455-464
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• 2011

최근들어 국내 설비투자의 증가세가 주춤한 데 반해 해외직접투자는 급격히 증가하면서 해외투자 급증세가 국내투자를 위축시키고 있는 것이 아닌가하는 우려가 증대되고 있다. 본고에서는 이러한 관점에 대해 경제성장 단계에 따른 환경변화를 감안하여 분석하였다. 분석을 위해서는 국내외 투자간의 관계를 회귀나무(regression tree) 기법을 이용하였다. 분석결과 과거에는 해외직접투자가 국내 설비투자를 대체하다가 수출기반의 급성장기에 접어들면서 보완하는 행태를 보인 것으로 나타났다. 그러나 외환위기 이후 해외직접투자가 국내 설비투자에 미치는 영향력이 사라진 것으로 추정되었다. 이러한 변화는 글로벌화가 진전에 따른 투자행태의 차별성 확대, 투자수익률이 높은 곳으로의 투자처 이전행태 가속화 등에 기인한것으로 볼 수 있다. 과거에는 지배적이었던 투자 결정 요인의 영향력이 약화되고 있는 것이다. 이는 한편으로 글로벌화에 우리 기업이 잘 적응해나가면서 투자의 유연성이 확대되었기 때문이라고 할 수 있다. 이러한 분석 결과를 감안하면 최근의 해외직접투자 증가가 일방적으로 국내투자를 제약한다는 제한된 시각에서 벗어나 투자를 평가할 필요가 있으며 우리 기업들의 해외직접투자가 국제적 생산네트워크를 형성하여 생산자원을 효율적으로 활용하는 방향으로 이루어 질 수 있도록 유도함으로써 국내외 투자간 적절한 보완관계를 형성해 나가는 것이 바람직하다 하겠다.

• Journal of the Korean Data and Information Science Society
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• 제14권2호
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• pp.177-186
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• 2003

The Zero-Inflated Poisson regression is a model for count data with exess zeros. When the correlated response variables are intrested, we have to extend the univariate zero-inflated regression model to multivariate model. In this paper, we study and simulate the multivariate zero-inflated regression model. A real example was applied to this model. Regression parameters are estimated by using MLE's. We also compare the fitness of multivariate zero-inflated Poisson regression model with the decision tree model.

• Journal of the Korean Data and Information Science Society
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• 제14권1호
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• pp.45-53
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• 2003

The Zero-Inflated Poisson regression is a model for count data with exess zeros. When the reponse variables have excess zeros, it is not easy to apply the Poisson regression model. In this paper, we study and simulate the zero-inflated Poisson regression model. An real example was applied to this model. Regression parameters are estimated by using MLE's. We also compare the fitness of zero-inflated Poisson model with the Poisson regression and decision tree model.

• Computers and Concrete
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• 제21권4호
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• pp.407-417
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• 2018

Concrete which is a composite material is one of the most important construction materials. Compressive strength is a commonly used parameter for the assessment of concrete quality. Accurate prediction of concrete compressive strength is an important issue. In this study, we utilized an experimental procedure for the assessment of concrete quality. Firstly, the concrete mix was prepared according to C 20 type concrete, and slump of fresh concrete was about 20 cm. After the placement of fresh concrete to formworks, compaction was achieved using a vibrating screed. After 28 day period, a total of 100 core samples having 75 mm diameter were extracted. On the core samples pulse velocity determination tests and compressive strength tests were performed. Besides, Windsor probe penetration tests and Schmidt hammer tests were also performed. After setting up the data set, twelve artificial intelligence (AI) models compared for predicting the concrete compressive strength. These models can be divided into three categories (i) Functions (i.e., Linear Regression, Simple Linear Regression, Multilayer Perceptron, Support Vector Regression), (ii) Lazy-Learning Algorithms (i.e., IBk Linear NN Search, KStar, Locally Weighted Learning) (iii) Tree-Based Learning Algorithms (i.e., Decision Stump, Model Trees Regression, Random Forest, Random Tree, Reduced Error Pruning Tree). Four evaluation processes, four validation implements (i.e., 10-fold cross validation, 5-fold cross validation, 10% split sample validation & 20% split sample validation) are used to examine the performance of predictive models. This study shows that machine learning regression techniques are promising tools for predicting compressive strength of concrete.