• Journal of the Korean Society for Library and Information Science
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• 제23권
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• pp.207-261
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• 1992

The purpose of this study is (1) to design and test a database which can be automatically classified, and (2) to generate automatic classification number by processing the keywords in titles using the code combination method of Colon Classification(CC) as well as an automatic recognition of subjects in order to develop an automatic classification system (Auto BC System) based on CC which can be applied to any research library. To conduct this study, 1,510 words in the fields of agricultrue and medicine were selected, analized in terms of [P], [M], [E], [S], [T] employed in CC, and included in a database for classification. For the above-mentioned subject fields, the principle of an automatic classification was specified in order to generate automatic classification codes as well as to perform an automatic subject recognition of the titles included. Whenever necessary, editing, deleting, appending and reindexing of a database can be made in this automatic classification system. Appendix 1 shows the result of the automatic classification of books in the fields of agriculture and medicine. The results of the study are summarized below. 1. The classification number for the title of a book can be automatically generated by using the facet principles of Colon Classification. 2. The automatic subject recognition of a book is achieved by designing a database making use of a globe-principle, and by specifying the subject field for each word. 3. The automatic subject-recognition of input data is achieved by measuring the number of searched words by each subject field. 4. The combination of classification numbers is achieved by flowcharting of classification formular of each subject field. 5. The efficient control of classification numbers is achieved by designing control codes on the database for classification. 6. The automatic classification by means of Auto BC has been proved to be successful in the research library concentrating on a Single field. The general library may have some problem in employing this system. The automatic classification through Auto BC has the following advantages: 1. Speed of the classification process can be improve. 2. The revision or updating of classification schemes can be facilitated. 3. Multiple concepts can be expressed in a single classification code. 4. The consistency of classification can be achieved with the classification formular rather than the classifier's subjective judgement. 5. A user's retrieving process can be made after combining the classification numbers through keywords relating to the material to be searched. 6. The materials can be classified by a librarian without subject backgrounds. 7. The large body of materials can be quickly classified by means of a machine processing. 8. This automatic classification is expected to make a good contribution to design of the total system for library operations. 9. The information flow among libraries can be promoted owing to the use of the same program for the automatic classification.

• Journal of information and communication convergence engineering
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• 제3권4호
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• pp.209-212
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• 2005

This study proposes hybrid case-based reasoning and genetic algorithms model for customer classification. In this study, vertical and horizontal dimensions of the research data are reduced through integrated feature and instance selection process using genetic algorithms. We applied the proposed model to customer classification model which utilizes customers' demographic characteristics as inputs to predict their buying behavior for the specific product. Experimental results show that the proposed model may improve the classification accuracy and outperform various optimization models of typical CBR system.

• Journal of Integrative Natural Science
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• 제13권4호
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• pp.132-140
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• 2020

With the recent introduction of artificial intelligence (AI) technology, the use of data is rapidly increasing, and newly generated data is also rapidly increasing. In order to obtain the results to be analyzed based on these data, the first thing to do is to classify the data well. However, when classifying data, if only one classification technique belonging to the machine learning technique is applied to classify and analyze it, an error of overfitting can be accompanied. In order to reduce or minimize the problems caused by misclassification of the classification system such as overfitting, it is necessary to derive an optimal classification by comparing the results of each classification by applying several classification techniques. If you try to interpret the data with only one classification technique, you will have poor reasoning and poor predictions of results. This study seeks to find a method for optimally classifying data by looking at data from various perspectives and applying various classification techniques such as LDA and QDA, such as linear or nonlinear classification, as a process before data analysis in data analysis. In order to obtain the reliability and sophistication of statistics as a result of big data analysis, it is necessary to analyze the meaning of each variable and the correlation between the variables. If the data is classified differently from the hypothesis test from the beginning, even if the analysis is performed well, unreliable results will be obtained. In other words, prior to big data analysis, it is necessary to ensure that data is well classified to suit the purpose of analysis. This is a process that must be performed before reaching the result by analyzing the data, and it may be a method of optimal data classification.

• Journal of Korean Academy of Nursing Administration
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• 제8권4호
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• pp.633-643
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• 2002

Purpose : This study was conducted to develop a patient classification system for hemodialysis and to test its validity and reliability. Method : The process of the system development was as below. The lists of hemodialysis nursing activities were collected from literature and hemodialysis practice guideline and they were classified into 10 factors and 16 elements. And then, 4 classification levels were identified for each element. The content validity and interrater reliability of developed patient classification system were tested. Result & Conclusion : 10 factors of patient classification system for hemodialysis were consisted of psychosocial support, mobility, access, teaching, assessment, stability, supportive therapy, test, general nursing during hemodialysis, hemodialysis room management. According to validity and reliability results and experts' opinions, 4 classification levels revised to 3 classification levels and 2 elements were deleted. Finally, patient classification system were consisted of 10 factors, 14 elements, 3 classification levels, 3 categories.

• Journal of Intelligence and Information Systems
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• 제1권1호
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• pp.61-72
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• 1995

Dynamic and explicit ordering of strategies is a key process in modeling knowledge-level problem-solving behavior. This paper addressed the important problem of howl to make the scheduler more knowledge-intensive in a way that facilitates the acquisition, integration, and maintenance of the scheduler control knowledge. The solution a, pp.oach described in this paper involved formulating the scheduler task as a heuristic classification problem, and then implementing it as a classification expert system. By doing this, the wide spectrum of known methods of acquiring, refining, and maintaining the knowledge of a classification expert system are a, pp.icable to the scheduler control knowledge. One important innovation of this research is that of recursive heuristic classification : this paper demonstrates that it is possible to formulate and solve a key subcomponent of heuristic classification as heuristic classification problem. Another key innovation is the creation of a method of dynamic heuristic classification : the classification alternatives that are selected among are dynamically generated in real-time and then evidence is gathered for and aginst these alternatives. In contrast, the normal model of heuristic classification is that of structured selection between a set of preenumerated fixed alternatives.

• Proceedings of the Korean Information Science Society Conference
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• 한국정보과학회 2007년도 가을 학술발표논문집 Vol.34 No.2 (C)
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• pp.485-489
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• 2007

Gaussian process classifiers (GPCs) are fully statistical kernel classification models which have a latent function with Gaussian process prior Recently, EP approximation method has been proposed to infer the posterior over the latent function. It can have a special hyperparameter which can treat outliers potentially. In this paper, we propose the outlier robust algorithm which alternates EP and the hyperparameter updating until convergence. We also show its usefulness with the simulation results.

• Journal of Korean Library and Information Science Society
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• 제11권
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• pp.175-209
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• 1984

The purpose of this study is to build a general principle for the automatic book-classification which can be put to use in library operation, and to present a methodology of the automatic classification for the library. Since the enumerative classification scheme which exist as manual systems cannot be a n.0, pplied to the automation of classification, the principles of Colon Classification by S.R. Ranganathan is brought in and studied. The result of the study can be summarized as follows: (1) Automatic book-classification can be performed by the principles of faceted classification. (2) This study presents a general and an a n.0, pplication principles for the automatic book-classification. (3) File design for the automatic book-classification of a general classification is different from that of special classification, (4) The methodology is to classify the literature by inputting the title into a terminal. In addition, the expected Value from the Automatic Book-classification is as follows: (1) The prompt and accurate process of classification is possible. (2) Though a book is classified in any library it can have the same classification number. (3) The user can retrieve the classification code of a book for which he or she wants to search through the dialogue with the computer. (4) Since the concept coordination method is employed, even the representing of a multi-subject concept is made simple. (5) By performing automatic book-classification, the automation of library operation can be completed.

• Journal of Korean Library and Information Science Society
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• 제33권1호
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• pp.143-167
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• 2002

The aim of this study is to examine the influence of foreign library classification schemes on the Chinese classification systems in the library. And the study is to analyze development process of three modem library classification schemes, the $\ulcorner$Renmin University of China's Library Books Classifications$\lrcorner$, $\ulcorner$Chinese Academy of Sciences's Library Books Classifications$\lrcorner$and $\ulcorner$Chinese Library Classification$\lrcorner$which are being used in many libraries in China where the library is regarded an important organization for performing the national policies.

• Journal of the Korean Operations Research and Management Science Society
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• 제38권1호
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• pp.139-152
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• 2013

The purpose of this study is to claim the validity of tuning the architecture of neural network models for multi-class classification. A neural network model for multi-class classification is basically constructed by building a series of neural network models for binary classification. Building a neural network model, we are required to set the values of parameters such as number of hidden nodes and weight decay parameter in advance, which draws special attention as the performance of the model can be quite different by the values of the parameters. For better performance of the model, it is absolutely necessary to have a prior process of tuning the parameters every time the neural network model is built. Nonetheless, previous studies have not mentioned the necessity of the tuning process or proved its validity. In this study, we claim that we should tune the parameters every time we build the neural network model for multi-class classification. Through empirical analysis using wine data, we show that the performance of the model with the tuned parameters is superior to those of untuned models.

• Journal of Korea Multimedia Society
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• 제19권1호
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• pp.86-95
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• 2016

The main objective of HACCP(Hazard analysis and critical control points) system is to provide a systematic preventive approach how to control the risks in food production process. Practically, the diet classification process performed at the one of the beginning steps of the HACCP system, makes an important role of determining food safety risks and how to control them in every control point according to the different risk level of the diet. In this paper, we propose an automatic diet classification method for HACCP system using XML(eXtensible Markup Language). In order to guarantee the diet classification accuracy, we design the XML schema and attributes represents the relationship of every diet and ingredients analysing the HACCP diet classification rules. Based on the XML schema and document generation method, we develope the proposed system as client and server model that implemented XML based HACCP diet information generation module and integrated HACCP information management modules, respectively. Moreover, we show the efficiency of the proposed system with experiment results describing the school food diet information as XML documents and parsing the diet information.