• Journal of Korean Society of Archives and Records Management
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• v.9 no.1
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• pp.151-173
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• 2009

The classification in the Records and Archives Sciences focuses on the contextual information in producing and utilizing records rather than their contents. This study aimed at designing an automatic records classification system to enable an automatic classification focusing on the aggregation of the context of records rather than the contents of individual record in the classification scheme, structured on the basis of business activities analyses for records reflecting the business activities. The automatic records classification system was designed to have mutual supplements by constructing the classification scheme and thesaurus together as the classification reference, as well as the aggregation of records that have been already classified. Additionally included are plans to apply the classified contextual information of records to the classification reference on the real-time base right after the category assignment of records to be classified. Although there are limitations as the designed system depends on the quality of the contextual information, it is considered that the system could lead to ensure that the contextual information of records should be more substantial.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.29 no.4
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• pp.297-317
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• 2018

The purpose of this study is to propose a classification system that can classify various industrial and craft materials consistently and systematically by comparing and analyzing the classification system of materials libraries in domestic and foreign. For this study, it was investigated the operation cases and classification system of domestic and foreign material libraries, and then proposed a method to classify consistently and systematically by comparing and analyzing the classification items of KDC and DDC. The classification system of material library was not classified by subject, but classified into the name of material and type, or the classification system of a material library was divided by Arabic numeral as a general classification system. It is difficult to access and share information because most of material libraries use the classification system developed by the library itself. Therefore, it is necessary to develop a standard classification system to share the information stored in the material libraries.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.2
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• pp.269-288
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• 2024

To structure the KOS registry, it is necessary to select a classification system that suits the characteristics of the collected KOS. This study aimed to classify domestic KOS collected through various classification schems, and based on these results, provide insights for selecting a classification system when structuring the KOS registry. A total of 313 KOS data collected via web searches were categorized using five types of classification systems and a thesaurus, and the results were analyzed. The analysis indicated that for international linkage of the KOS registry, foreign classification systems should be applied, and for optimization with domestic knowledge resources or to cater to domestic researchers, domestic classification systems need to be applied. Additionally, depending on the field-specific characteristics of the KOS, research area KOS should apply classification systems based on academic disciplines, while public sector KOS should consider classification systems based on government functions. Lastly, it is necessary to strengthen the linkage between domestic and international KOS, which also requires the application of multiple classification systems.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.2
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• pp.517-524
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• 2010

Recently, the size of online texts and textual information is increasing explosively, and the automated classification has a great potential for handling data such as news materials and images. Text classification system is based on supervised learning which needs laborous work by human expert. The main goal of this paper is to reduce the manual intervention, required for the task. The other goal is to increase accuracy to be high. Most of the documents have high complexity in contents and the high similarities in their described style. So, the classification results are not satisfactory. This paper shows the implementation of classification system based on ETOM+RPost algorithm and classification progress using SPAM data. In experiments, we verified our system with right-training documents and wrong-training documents. The experimental results show that our system has high accuracy and stability in all situation as 16% improvement in accuracy.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.123-130
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• 2022

Recently, studies on the detection and classification of Android malware based on API Call sequence have been actively carried out. However, API Call sequence based malware classification has serious limitations such as excessive time and resource consumption in terms of malware analysis and learning model construction due to the vast amount of data and high-dimensional characteristic of features. In this study, we analyzed various classification models such as LightGBM, Random Forest, and k-Nearest Neighbors after significantly reducing the dimension of features using PCA(Principal Component Analysis) for CICAndMal2020 dataset containing vast API Call information. The experimental result shows that PCA significantly reduces the dimension of features while maintaining the characteristics of the original data and achieves efficient malware classification performance. Both binary classification and multi-class classification achieve higher levels of accuracy than previous studies, even if the data characteristics were reduced to less than 1% of the total size.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.959-971
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• 2019

CNN (Convolutional Neural Network) is one representative deep learning algorithm, which can extract high-level spatial and spectral features, and has been applied for hyperspectral image classification. However, one significant drawback behind the application of CNNs in hyperspectral images is the high dimensionality of the data, which increases the training time and processing complexity. To address this problem, several CNN based hyperspectral image classification studies have exploited PCA (Principal Component Analysis) for dimensionality reduction. One limitation to this is that the spectral information of the original image can be lost through PCA. Although it is clear that the use of PCA affects the accuracy and the CNN training time, the impact of PCA for CNN based hyperspectral image classification has been understudied. The purpose of this study is to analyze the quantitative effect of PCA in CNN for hyperspectral image classification. The hyperspectral images were first transformed through PCA and applied into the CNN model by varying the size of the reduced dimensionality. In addition, 2D-CNN and 3D-CNN frameworks were applied to analyze the sensitivity of the PCA with respect to the convolution kernel in the model. Experimental results were evaluated based on classification accuracy, learning time, variance ratio, and training process. The size of the reduced dimensionality was the most efficient when the explained variance ratio recorded 99.7%~99.8%. Since the 3D kernel had higher classification accuracy in the original-CNN than the PCA-CNN in comparison to the 2D-CNN, the results revealed that the dimensionality reduction was relatively less effective in 3D kernel.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.33 no.2
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• pp.55-73
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• 2022

This study investigated the cases of the domestic classification system and compared and analyzed them with the academic standard classification system to derive future improvement directions. The direction of future improvement of the academic standard classification system presented based on this is as follows. First, it seems necessary to clearly guarantee the operation of the classification system as a law for the continuous development of the academic standard classification system. Second, it is necessary to improve it to a comprehensive classification principle that satisfies both current issues and global universality so that domestic and foreign data can be collected and compared smoothly by producing a wide-ranging classification system. Third, it is necessary to select a clear revision cycle of the academic standard classification system, and it seems appropriate to proceed with the revision every five years in order to reflect the academic field across a vast field. Currently, research on such a domestic classification system is insufficient, and such investigations are continuously conducted in the future, requiring continuous interest and research on the domestic classification system.

• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.223-236
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• 2013

Various statistical classification methods to predict election to the Major League Baseball hall of fame of are implemented and their accuracies are compared. Seventeen independent variables are selected from the data of candidates eligible for the hall of fame and well-known classification methods such as discriminant analysis and logistic regression as well as the recently proposed Mahalanobis-Taguchi system(MTS). The MTS showed a better performance than the others in classification accuracy because it is especially efficient in cases where multivariate data does not constitute directionally geographical groups according to attributes.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.190-193
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• 2000

In this paper, we investigate the distribution of classification accuracies of multiclass problems in the feature space and analyze performances of the conventional feature extraction algorithms. In order to find the distribution of classification accuracies, we sample the feature space and compute the classification accuracy corresponding to each sampling point. Experimental results showed that there exist much better feature sets that the conventional feature extraction algorithms fail to find. In addition, the distribution of classification accuracies is useful for developing and evaluating the feature extraction algorithm.

• Journal of Korean Society for Quality Management
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• v.23 no.2
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• pp.53-67
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• 1995

The problem considered here is to find the optimal discriminant analysis method in growth curve model. It has been studied how to find correct prior probability for the effective classification in discriminant analysis. We use the balanced condition to calculate prior probability. From the informative simulation study, new classification rule for the growth curve model is suggested. The suggested classification rule has better classification result than the other previously suggested method in terms of error rate criterion.