• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.48 no.1
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• pp.34-42
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• 2016

In this study, we classified three copying papers and Korean, Chinese, and Japanese traditional papers using IR and/or NIR spectra and principal component analysis. Various chemicals are used when producing fine papers. In this case, the IR method to analyze functional groups is suitable for the classification of paper. On the other hand, NIR analysis is more suitable for the classification of traditional papers, as it uses nearly raw materials (pulp). Therefore, principal component analysis using IR and NIR depending on the paper production process will be the classification tool of paper.

• Journal of Korean Society of Rural Planning
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• v.11 no.2 s.27
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• pp.35-41
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• 2005

It is necessary that the basic concept of rural planning update from economics based on the production and sale into experience of natural resources and traditional culture. For the purpose of set up development direction for rural district, it is requisite to the multivariate analysis. In this study, the methods of the classification of rural village with existing data are studied, the results looking for applying to the making of principal viewpoint of the development. The analysis methods of classification are used the PCA, CA and combination of these, and making the revised method for localization of the rural district. In this study, we implement classification of regional pattern analysis for the planning of rural district in Chungbuk province.

• Journal of the Korean Society of Safety
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• v.32 no.5
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• pp.179-185
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• 2017

Accident analysis is an essential process to make basic data for accident prevention. Most researches depend on survey data and accident statistics to analyze accidents, but these kinds of data are not sufficient for systematic and detailed analysis. We, in this paper, propose an accident classification model that extracts task type, original cause materials, accident type, and the number of deaths from accident reports. The classification model is a support vector machine (SVM) with word occurrence features, and these features are selected based on mutual information. Experiment shows that the proposed model can extract task type, original cause materials, accident type, and the number of deaths with almost 100% accuracy. We also develop an accident ontology to express the information extracted by the classification model. Finally, we illustrate how the proposed classification model and ontology effectively works for the accident analysis. The classification model and ontology are expected to effectively analyze various accidents.

• Journal of the Korean Society of Earth Science Education
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• v.4 no.2
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• pp.151-165
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• 2011

Following a microgenetic design, this study was analysed the characteristic and the change of classification strategy that appear in college students' classification activity. The 4 tasks were developed for classification activity; a shell as a familiar real things, an animal fossil as a unfamiliar real things, a snow flake as a familiar picture cards and galaxy as a unfamiliar picture card. Achieved study to 6 college students who major in elementary education. Data were collected by interview with subjects, subject's classification schema, investigator's observation of subject's activity, and videotaped that record subject's subject classification process over an extended period of 6 times. Result proved in this study is as following. In the 6 times of the data collection procedures, a strategy F identifying concrete attribution of classification objects and a more detailed strategy X3 combining qualitative, spatial and dimensional attribution were found and more frequently used in both groups of college students which reported a classification process and did not report the process. While discovery and absorption of both a concrete classification strategy and a detailed classification strategy were rapidly developed in the reporting group, they were gradually developed in the non-reporting group. In addition to this, as the data collection procedures were progressing, the college students were familiar with change factors of classification tasks and in the case of pictures the classification strategy showed more desirable changes.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1103-1113
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• 2011

Gene expression data is obtained through many stages of an experiment and errors produced during the process may cause missing values. Due to the distinctness of the data so called 'small n large p', genes have to be selected for statistical analysis, like classification analysis. For this reason, imputation and gene selection are important in a microarray data analysis. In the literature, imputation, gene selection and classification analysis have been studied respectively. However, imputation, gene selection and classification analysis are sequential processing. For this aspect, we compare the performance of classification methods after imputation and gene selection methods are applied to microarray data. Numerical simulations are carried out to evaluate the classification methods that use various combinations of the imputation and gene selection methods.

• Journal of the Speleological Society of Korea
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• no.84
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• pp.1-9
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• 2008

Since the existing cave type classification has no variety but was limited to the structural, genetical and dimensional classification, we need the new cave type classification. When we analyze the theoretical background of cluster analysis, the cave type can be classified in consideration of diverse variables depending on the selection of variables to use and the usability of such classification is very high. With the practical consideration on the internal environment of cave and surrounding environment, three classifications are available; first, numerical classification by the dimension and form of cave; second, classification by the use of land out of the cave and geographic features; third, classification by the feature of location related to the surrounding areas of cave.

• Communications for Statistical Applications and Methods
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• v.11 no.2
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• pp.275-285
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• 2004

We explore classification analysis using graphical methods such as sliced inverse regression and sliced average variance estimation based on dimension reduction. Some useful information about classification analysis are obtained by sliced inverse regression and sliced average variance estimation through dimension reduction. Two examples are illustrated, and classification rates by sliced inverse regression and sliced average variance estimation are compared with those by discriminant analysis and logistic regression.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.248-251
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• 1997

In this paper, We propose the concept of approximate Classification in the field of two group discriminan analysis. In our approach, an attribute space is divided into three subspaces. Two subspaces are for given two group and one subspace is for a boundary area between the two groups. We propose Approximate Pattern Classification with Rough set. We also propose learning procedures of neural networks for approximate classification. We propose two weighting methods which lead to possibility analysis and necessity analysis. We illustrate the proposed methods by numerical examples.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.909-917
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• 2003

Although many classification models have been used to classify binary data, none of the classification models dominates all varying circumstances depending on the number of variables and the size of data(Asparoukhov and Krzanowski (2001)). This paper proposes a classification model which uses information on marginal distributions of sub-variables and its maximum entropy distribution. Classification experiments by using simulation are discussed.

• The Korean Journal of Applied Statistics
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• v.23 no.6
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• pp.1093-1102
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• 2010

Even though the k-Nearest Neighbors Classification(KNNC) is one of the popular non-parametric classification methods, it does not consider the local features and class information for each observation. In order to overcome such limitations, several methods have been developed such as Adaptive Nearest Neighbors Classification(ANNC) and Modified k-Nearest Neighbors Classification(MKNNC). In this paper, we propose the Modified Adaptive Nearest Neighbors Classification(MANNC) that employs the advantages of both the ANNC and MKNNC. Through a real data analysis and a simulation study, we show that the proposed MANNC outperforms other methods in terms of classification accuracy.