• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.1
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• pp.55-74
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• 2005

Bankruptcy prediction has drawn a lot of research interests in previous literature, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper employs a relatively new machine learning technique, support vector machines (SVMs). to bankruptcy prediction problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, we use grid search technique using 5-fold cross-validation to find out the optimal values of the parameters of kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM. we compare its performance with multiple discriminant analysis (MDA), logistic regression analysis (Logit), and three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the other methods.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.543-553
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• 2005

Linear logistic regression is one of the most widely used method for credit scoring in credit risk management. This paper deals with credit scoring using splines based on Logistic regression. Linear splines and an automatic basis selection algorithm are adopted. The final model is an example of the generalized additive model. A simulation using a real data set is used to illustrate the performance of the spline method.

• Journal of Physiology & Pathology in Korean Medicine
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• v.20 no.1
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• pp.268-272
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• 2006

In this study, when physician make a diagnosis of Sasang constitution of patients, anthropometric data are applied to seek the classification function into Sasang constitution. Data used in the analysis are the anthropometric data of 550 patients who had been treated in nine oriental medical hospital, and our data have no missing value in 12 anthropometric variables. In order to improve the accuracy of classification function into Sasang constitution, we consider one method of variable transformation of anthropometric data based on oriental medicine.

• Korean Journal of Oriental Medicine
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• v.13 no.2 s.20
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• pp.59-63
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• 2007

The purpose of this study was to diagnosis that what patterns identification using the statistical method. Discriminant analysis using the medical specialist and resident pattern identification agree case in stroke patients within 1 month of onset. The agreement rate of dificiency of Gi(75%), heat-transformation(74%), dampphlegm syndrome(69%), deficiency of Eum(51%) and syndrome of blood stagnation(43%) are respectively 0.75, 0.74, 0.69, 0.51 and 0.43 in medical specialist and using linear discriminant function pattern identification are same. The study of inspection, pulse feeling and palpitation will be continued to evaluate concordance rate. Discrimination model will be make to get higher Accuracy and prediction, it means becomes the help in pattern identification diagnosis objectivity and scientific.

• Health Policy and Management
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• v.12 no.2
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• pp.1-22
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• 2002

Since the early 1980s scholars have applied latent structure and other type of finite mixture models from various academic fields. Although the merits of finite mixture model are well documented, the attempt to apply the mixture model to medical service has been relatively rare. The researchers aim to try to fill this gap by introducing finite mixture model and segmenting inpatients DB from one general hospital. In section 2 finite mixture models are compared with clustering, chi-square analysis, and discriminant analysis based on Wedel and Kamakura(2000)'s segmentation methodology schemata. The mixture model shows the optimal segments number and fuzzy classification for each observation by EM(expectation-maximization algorism). The finite mixture model is to unfix the sample, to Identify the groups, and to estimate the parameters of the density function underlying the observed data within each group. In section 3 and 4 we illustrate results of segmenting 4510 patients data including menial and ratio scales. And then, we show AHP can be identify the attractiveness of each segment, in which the decision maker can select the best target segment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.744-752
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• 2012

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.200-206
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• 2024

Computing music similarity is an indispensable component in developing music search service. This paper proposes a relevance weight of each chromagram vector for cover song identification in computing a music similarity function in order to boost identification accuracy. We derive a music similarity function using the relevance weight based on the probabilistic relevance model, where higher relevance weights are assigned to less frequently-occurring discriminant chromagram vectors while lower weights to more frequently-occurring ones. Experimental results performed on two cover music datasets show that the proposed music similarity improves the cover song identification performance.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.7 no.2
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• pp.53-61
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• 1989

In this paper the quantitative classification parameters of terrain which can be practicable to the interpolation of digital terrain model forming a regular grid pattern have been suggested and objective terrain classification have been established by making a cluster analysis using these parameters. Also, interpolation suitable to the classification of terrain has been used by making a descriminant alaysis from description parameters of terrains. The terrain classification in this paper was dependent upon two parameters of the ratio horizontal area to inclined area and the magnitude of harmonic vectors. And the studying area was seperated to three groups of terrains by these two parameters. Three groups of terrains could be classified into the discriminant functions. By determining the ratio of area and harmonic vector magnitude in any terrains using the above discriminant function, it was possible to discriminate the terrains to apply the interpolation practicable to the terrain characteristics.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.237-250
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• 2017

A new field of e-sports gains the great popularity in Korea as well as abroad. AOS (aeon of strife) genre games are quickly gaining popularity with gamers from all over the world and the game companies hold game competitions. The e-sports broadcasting teams and webzines use a variety of statistical indicators. In this paper, as an AOS genre game, League of Legends game data is used for statistical analysis using the indicators to predict the outcome. We develop new indicators with the factor analysis to improve existing indicators. Also we consider discriminant function, neural network model, and SVM (support vector machine) for make winning forecasting models. As a result, the new position indicators reflect the nature of the role in the game and winning forecasting models show more than 95 percent accuracy.

• Food Science of Animal Resources
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• v.32 no.5
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• pp.669-677
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• 2012

The objective of this study was to analyze the flavor pattern of different varieties of cheeses. Four of the each following cheese varieties such as shred type pizza cheese, Cheddar cheese, Mozzarella block cheese, and white mold-ripened cheeses, stored at $4^{\circ}C$ during 2 wks were examined before and after cooking at $70^{\circ}C$ and $160^{\circ}C$. Flavor patterns of these cheeses were analyzed using an electronic nose system based on mass spectrometer. All data were treated by multivariate data processing based on discriminant function analysis (DFA). The results showed the discriminant model by DFA method. Data revealed that flavor patterns of pizza cheeses were well separated as storage prolonged and obviously discriminated as the higher the cooking temperature. The result of pattern recognition analysis based on discriminant function analysis showed that new brand of pizza cheese produced by Imsil Cheese Cooperative was located at middle between the flavors of the imported brands of pizza cheese and those of domestic brand of pizza cheeses. Imsil cheese has a unique flavor pattern among other variety of cheeses. Application of pattern recognition analysis by electronic nose might be useful and advanced technology for characterizing in flavor pattern of cheese products from different origins and different categories of cheeses.