• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.931-940
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• 2011

Classification is to generate a rule of classifying objects into several categories based on the learning sample. Good classification model should classify new objects with low misclassification error. Many types of classification methods have been developed including logistic regression, discriminant analysis and tree. This paper presents a new classification method using penalized partial least squares. Penalized partial least squares can make the model more robust and remedy multicollinearity problem. This paper compares the proposed method with logistic regression and PCA based discriminant analysis by some real and artificial data. It is concluded that the new method has better power as compared with other methods.

• Journal of Korea Multimedia Society
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• v.7 no.7
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• pp.877-885
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• 2004

In this paper, we propose an enhancement shot change detection method using the neural net and the robust feature selection out of multiple features. The previous shot change detection methods usually used single feature and fixed threshold between consecutive frames. However, contents such as color, shape, background, and texture change simultaneously at shot change points in a video sequence. Therefore, in this paper, we detect the shot changes effectively using robust features, which are supplementary each other, rather than using single feature. In this paper, we use the typical CART (classification and regression tree) of data mining method to select the robust features, and the backpropagation neural net to determine the threshold of the each selected features. And to evaluation the performance of the robust feature selection, we compare the proposed method to the PCA(principal component analysis) method of the typical feature selection. According to the experimental result. it was revealed that the performance of our method had better that than the PCA method.

• Survey Research
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• v.4 no.2
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• pp.1-21
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• 2003

We proposed imputation strategies for marital status in the Population and Housing Census 2000 in Korea to illustrate the effective missing value imputation methods for social survey. The marital status which have relatively high non-response rates in the Census are considered to develope the effective missing value imputation procedures. The Classification and Regression Tree(CART)is employed to construct the imputation cells for hot-deck imputation, as well as to predict the missing value by model-based approach. We compare to imputation methods which include the CART model-based imputation and the sequential hot-deck imputation based on CART. Also we check whether different modeling for each region provides the more improved results. The results suggest that the proposed hot-deck imputation based on CART is very efficient and strongly recommendable. And the results show that different modeling for each region is not necessary.

• Safety and Health at Work
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• v.1 no.2
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• pp.140-148
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• 2010

Objectives: Determining the work-relatedness of lung cancer developed through occupational exposures is very difficult. Aims of the present study are to develop a decision tree of occupational lung cancer. Methods: 153 cases of lung cancer surveyed by the Occupational Safety and Health Research Institute (OSHRI) from 1992-2007 were included. The target variable was whether the case was approved as work-related lung cancer, and independent variables were age, sex, pack-years of smoking, histological type, type of industry, latency, working period and exposure material in the workplace. The Classification and Regression Test (CART) model was used in searching for predictors of occupational lung cancer. Results: In the CART model, the best predictor was exposure to known lung carcinogens. The second best predictor was 8.6 years or higher latency and the third best predictor was smoking history of less than 11.25 pack-years. The CART model must be used sparingly in deciding the work-relatedness of lung cancer because it is not absolute. Conclusion: We found that exposure to lung carcinogens, latency and smoking history were predictive factors of approval for occupational lung cancer. Further studies for work-relatedness of occupational disease are needed.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.526-533
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• 2011

Contracting is said to be a high-risk business, and a common cause of business failure is related to cash management. A contractor's financial viability depends heavily on how actual payments from an owner deviate from those defined in the contract. The paper presents a method for contractors to evaluate the punctuality and fullness of owner payments based on historical behaviour. It does this by classifying owners according to their late and incomplete payment practices. A payment profile of an owner, in the form of aging claims submitted by the contractor, is used as a basis for the method's development. Regression trees are constructed based on three predictor variables, namely, the average time to payment following a claim, the total amount ending up being paid within a certain period and the level of variability in claim response times. The Tree package in the publicly available R program is used for building the trees. The analysis is particularly useful for contractors at the pre-tendering stage, when contractors predict the likely payment scenario in an upcoming project. Based on the method, the contractor can decide whether to tender or not tender, or adjust its financial preparations accordingly. The paper is a contribution in risk management applied to claim and dispute resolution practice. It is argued that by contractors having a better understanding of owner payment behaviour, fewer disputes and contractor business failures will occur.

• The Journal of the Acoustical Society of Korea
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• v.18 no.4
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• pp.60-64
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• 1999

In this paper, we apply CART(Classification And Regression tree) and OC1(Oblique Classifier1) which methods are widely used for continuous speech recognition and synthesis. We prediet prosodic boundary index by applying CART and OC1, which combine right depth of tree-structured method and To_Right of link grammar method with tri_gram model. We assigned four prosodic boundary index level from 0 to 3. Experimental results show that OC1 method is superior to CART method. In other words, in spite of OC1's having fewer nodes than CART, it can make more improved prediction than CART.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.13-20
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• 2022

It's proposed and analyzed ML(Machine Learning) models to predict vehicle FC(Fuel Consumption) in real-time. The test driving was done for a car to measure vehicle speed, acceleration, road gradient and FC for training dataset. The various ML models were trained with feature data of speed, acceleration and road-gradient for target FC. There are two kind of ML models and one is regression type of linear regression and k-nearest neighbors regression and the other is classification type of k-nearest neighbors classifier, logistic regression, decision tree, random forest and gradient boosting in the study. The prediction accuracy is low in range of 0.5 ~ 0.6 for real-time FC and the classification type is more accurate than the regression ones. The prediction error for total FC has very low value of about 0.2 ~ 2.0% and regression models are more accurate than classification ones. It's for the coefficient of determination (R²) of accuracy score distributing predicted values along mean of targets as the coefficient decreases. Therefore regression models are good for total FC and classification ones are proper for real-time FC prediction.

• Journal of Service Research and Studies
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• v.3 no.1
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• pp.9-16
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• 2013

Data analysis is the universal classification techniques, which requires a lot of effort. It can be easily analyzed to understand the results. Decision tree which is developed by Breiman can be the most representative methods. There are two core contents in decision tree. One of the core content is to divide dimensional space of the independent variables repeatedly, Another is pruning using the data for evaluation. In classification problem, the response variables are categorical variables. It should be repeatedly splitting the dimension of the variable space into a multidimensional rectangular non overlapping share. Where the continuous variables, binary, or a scale of sequences, etc. varies. In this paper, we obtain the coefficients of precision, reproducibility and accuracy of the classification tree to classify and evaluate the performance of the new cases, and through experiments to evaluate.

• Journal of Korean Society for Quality Management
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• v.34 no.2
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• pp.129-135
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• 2006

In the advance of computer technology, it is possible to keep all the related informations for monitoring equipments in control and huge amount of real time manufacturing data in a data base. Thus, the statistical analysis of large data sets with hundreds of thousands observations and hundred of independent variables whose some of values are missing at many observations is needed even though it is a formidable computational task. A tree structured approach to classification is capable of screening important independent variables and their interactions. In a Six Sigma project handling large amount of manufacturing data, one of the goals is to screen vital few variables among trivial many variables. In this paper we have reviewed and summarized CART, C4.5 and CHAID algorithms and proposed a simple method of screening vital few variables by selecting common variables screened by all the three algorithms. Also how to develop a logistics regression model on a large data set is discussed and illustrated through a large finance data set collected by a credit bureau for th purpose of predicting the bankruptcy of the company.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.56-62
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• 2021

High forecast accuracy is required as social issues on particulate matter increase. Therefore, many attempts are being made using machine learning to increase the accuracy of particulate matter prediction. However, due to problems with the distribution of imbalance in the concentration and various characteristics of particulate matter, the learning of prediction models is not well done. In this paper, to solve these problems, a binary classification model was proposed to predict the concentration of particulate matter needed for prediction by dividing it into two classes based on the value of 80㎍/㎥. Four classification algorithms were utilized for the binary classification of PM10. Classification algorithms used logistic regression, decision tree, SVM, and MLP. As a result of performance evaluation through confusion matrix, the MLP model showed the highest binary classification performance with 89.98% accuracy among the four models.