• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.81-86
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• 2012

In this paper, we proposed a new design methodology of a pattern classification rule based on the local linear discriminant analysis expanded from the generic linear discriminant analysis which is used in the local area divided from the whole input space. There are two ways such as k-Means clustering method and the differential evolutionary algorithm to partition the whole input space into the several local areas. K-Means clustering method is the one of the unsupervised clustering methods and the differential evolutionary algorithm is the one of the optimization algorithms. In addition, the experimental application covers a comparative analysis including several previously commonly encountered methods.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.49-63
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• 1997

We consider the problem of classifying a p x 1 observation into one of two multivariate normal populations when the training smaples contain a block of missing observation. A new classification procedure is proposed which is a linear combination of two discriminant functions, one based on the complete samples and the other on the incomplete samples. The new discriminant function is easy to use.

• Journal of Physiology & Pathology in Korean Medicine
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• v.23 no.6
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• pp.1460-1464
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• 2009

In spite of many studies on statistical model for pattern identifications (PIs), little attention has been paid to the complexity of pattern diagnosis processed by oriental physicians. The aim of this study is to develop a statistical diagnostic model which discriminates four PIs using multiple indicators in stroke. Clinical data were collected from 981 stroke patients and 516 data of which PIs were agreed by two independent physicians were included. Discriminant analysis was carried out using clinical indicators such as symptoms and signs which referred to pattern diagnosis, and applied to validation samples which contained all symptoms and signs manifested. Four Fischer's linear discriminant models were derived and their accuracy and prediction rates were 93.2% and 80.43%, respectively. It is important to consider the pattern diagnosis processed by oriental physicians in developing statistical model for PIs. The discriminant model developed in this study using multiple indicators is valid, and can be used in the clinical fields.

• Journal of Biomedical Engineering Research
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• v.9 no.1
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• pp.93-100
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• 1988

The Auto Regressive Parameter Estimation and Pattern Classification of EKG Signal for Automatic Diagnosis. This paper presents the results from pattern discriminant analysis of an AR (auto regressive) model parameter group, which represents the HRV (heart rate variability) that is being considered as time series data. HRV data was extracted using the correct R-point of the EKG wave that was A/D converted from the I/O port both by hardware and software functions. Data number (N) and optimal (P), which were used for analysis, were determined by using Burg's maximum entropy method and Akaike's Information Criteria test. The representative values were extracted from the distribution of the results. In turn, these values were used as the index for determining the range o( pattern discriminant analysis. By carrying out pattern discriminant analysis, the performance of clustering was checked, creating the text pattern, where the clustering was optimum. The analysis results showed first that the HRV data were considered sufficient to ensure the stationarity of the data; next, that the patern discrimimant analysis was able to discriminate even though the optimal order of each syndrome was dissimilar.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.717-718
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• 2008

Fisher Linear Discriminant(FLD) is a sample and intuitive linear feature extraction method in pattern recognition. But in some special cases, such as un-separable case, one class data dispersed into several clustering case, FLD doesn't work well. In this paper, a new discriminant named K-means Fisher Linear Discriminant, which combines FLD with K-means clustering is proposed. It could deal with this case efficiently, not only possess FLD's global-view merit, but also K-means' local-view property. Finally, the simulation results also demonstrate its advantage against K-means and FLD individually.

• Journal of Physiology & Pathology in Korean Medicine
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• v.25 no.6
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• pp.1113-1118
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• 2011

In this paper, when a physician make a diagnosis of the pattern identification (PI) in Korean stroke patients, the development methods of the PI classification function is considered by diagnostic questionnaire of the PI for stroke patients. Clinical data collected from 1,502 stroke patients who was identically diagnosed for the PI subtypes diagnosed by two physicians with more than 3 years experiences in 13 oriental medical hospitals. In order to develop the classification function into PI using Korean Stroke Syndrome Differentiation Standard was consist of the 44 items (Fire heat(19), Qi deficiency(11), Yin deficiency(7), Dampness-phlegm(7)). Using the 44 items, we took diagnostic and prediction accuracy rate through of discriminant model. The overall diagnostic and prediction accuracy rate of the PI subtypes for discriminant model was 74.37%, 70.88% respectively.

• The Journal of Korean Obstetrics and Gynecology
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• v.22 no.1
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• pp.148-160
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• 2009

Purpose: We intended to observe the correlations between Primary dysmenorrhea severity and Questionnaires for Blood Stasis Pattern. Methods: After initial approval by Kyung-Hee University Oriental Medical Hospital Institutional Review Board of Clinical Trials, volunteers for the clinical trial were recruited. We selected the 52 primary dysmenorrhea patients by the screening tests (clinical examination and inquiry). The severity of dysmenorrhea was evaluated by VAS (Visual Analog Scale), VRS (Verbal Rating Scale) & MVRS (Multidimensional Verbal Rating Scale). The severity of Blood Stasis was evaluated by Questionnaires for Blood Stasis Pattern. For statistics, we used Spearman's rho correlations, SPSS 13.0 for windows. Results: In case of VAS, though two items (眼瞼下靑紫, 便黑) of Questionnaires for Blood Stasis Pattern were correlated, total score & discriminant function score of Questionnaires for Blood Stasis Pattern were not correlated. In case of VRS, though two items (小腹痛, 夜間痛) of Questionnaires for Blood Stasis Pattern were correlated, total score & discriminant function score of Questionnaires for Blood Stasis Pattern were not correlated. In case of MVRS, though one items (久痺症) of Questionnaires for Blood Stasis Pattern were correlated, total score & discriminant function score of Questionnaires for Blood Stasis Pattern were not correlated. Conclusion: Though the results showed partial correlation of Primary dysmenorrhea severity and Questionnaires for Blood Stasis Pattern, we need further study after improvement and complementation of Questionnaires for Blood Stasis Pattern.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.4
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• pp.534-540
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• 2014

In this paper, we propose a fuzzy combined Polynomial Neural Network(PNN) for pattern classification. The fuzzy combined PNN comes from the generic TSK fuzzy model with several linear polynomial as the consequent part and is the expanded version of the fuzzy model. The proposed pattern classifier has the polynomial neural networks as the consequent part, instead of the general linear polynomial. PNNs are implemented by stacking the simple polynomials dynamically. To implement one layer of PNNs, the various types of simple polynomials are used so that PNNs have flexibility and versatility. Although the structural complexity of the implemented PNNs is high, the PNNs become a high order-multi input polynomial finally. To estimate the coefficients of a polynomial neuron, The weighted linear discriminant analysis. The output of fuzzy rule system with PNNs as the consequent part is the linear combination of the output of several PNNs. To evaluate the classification ability of the proposed pattern classifier, we make some experiments with several machine learning data sets.

• Women's Health Nursing
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• v.15 no.4
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• pp.312-319
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• 2009

Purpose: The purpose of this study was to determine the discriminant factors of attitude pattern toward sexual violence of college women. Methods: A cross-sectional research design with non-probability samples was conducted. A total of 292 college women participated. The instruments were Attitude Pattern toward Sexual Violence, Self-Esteem Scale, Gender Role Scale, and Attitude toward Sexuality. Dependent variable is Attitude Pattern toward Sexual Violence, which is composed of two groups; cases either harmer blame or sufferer blame. Independent variables were self-esteem, attitude toward gender role, and attitude toward sexuality. Data were analyzed by SPSS WIN program and descriptive analysis, $x^2$-test, and discriminant analysis. Results: To assess the adequacy of classification, the overall hit ratio was 68.5%, and the significant predictor variable was attitude toward sexuality. Conclusion: Replication of the study needs to be considered to further enrich the specific knowledge base regarding attitude toward sexual violence among college women.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.642-645
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• 2009

Brain Computer Interface (BCI) is a communication pathway between devices (computers) and human brain. It treats brain signals in real-time basis and discriminates some information of what human brain is doing. In this work, we develop a EEG BCI system using a feature extraction such as common spatial pattern (CSP) and a classifier using Fisher linear discriminant analysis (FLDA). Two-class EEG motor imagery movement datasets with both cued and uncued are tested to verify its feasibility.