• Asia pacific journal of information systems
• /
• v.20 no.2
• /
• pp.23-37
• /
• 2010

Conventional Support Vector Machines (SVMs) have been utilized as classifiers for binary classification problems. However, certain real world problems, including corporate bond rating, cannot be addressed by binary classifiers because these are multi-class problems. For this reason, numerous studies have attempted to transform the original SVM into a multiclass classifier. These studies, however, have only considered nominal classification problems. Thus, these approaches have been limited by the existence of multiclass classification problems where classes are not nominal but ordinal in real world, such as corporate bond rating and multiclass customer classification. In this study, we adopt a novel multiclass SVM which can address ordinal classification problems using ordinal pairwise partitioning (OPP). The proposed model in our study may use fewer classifiers, but it classifies more accurately because it considers the characteristics of the order of the classes. Although it can be applied to all kinds of ordinal multiclass classification problems, most prior studies have applied it to finance area like bond rating. Thus, this study applies it to a real world customer level classification case for implementing customer relationship management. The result shows that the ordinal multiclass SVM model may also be effective for customer level classification.

• Communications for Statistical Applications and Methods
• /
• v.14 no.1
• /
• pp.133-140
• /
• 2007

The support vector machine has been well developed as a powerful tool for solving classification problems. In many real world applications, each training point has a different effect on constructing classification rule. Lin and Wang (2002) proposed fuzzy support vector machines for this kind of classification problems, which assign fuzzy memberships to the input data and reformulate the support vector classification. In this paper another intuitive approach is proposed by using the fuzzy ${\alpha}-cut$ set. It will show us the trend of classification functions as ${\alpha}$ changes.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2007.04a
• /
• pp.317-321
• /
• 2007

"The project for production of medium classification landcover maps using satellite images" has been completed from 1998 until 2005 in Korea. As the 5th project was finished in 2005, medium classification landcover maps for all areas of South Korea have been produced. The products of project currently is used in lots of fields such as public governments, universities and research institutes for policy application and scientific research. But final results of the project have several problems which is insufficiency of reliability, discordance of classification codes and many others because each project was progressed year by year. In this study, problems of existing production methods about medium classification landcover maps are extracted and solution of problems is offered. Therefore, this study will make it possible to efficiently produce and update medium classification landcover maps.

• Industrial Engineering and Management Systems
• /
• v.11 no.1
• /
• pp.87-93
• /
• 2012

This paper discusses a new weighting method for text analyzing from the view point of supervised learning. The term frequency and inverse term frequency measure (tf-idf measure) is famous weighting method for information retrieval, and this method can be used for text analyzing either. However, it is an experimental weighting method for information retrieval whose effectiveness is not clarified from the theoretical viewpoints. Therefore, other effective weighting measure may be obtained for document classification problems. In this study, we propose the optimal weighting method for document classification problems from the view point of supervised learning. The proposed measure is more suitable for the text classification problem as used training data than the tf-idf measure. The effectiveness of our proposal is clarified by simulation experiments for the text classification problems of newspaper article and the customer review which is posted on the web site.

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

• Perspectives in Nursing Science
• /
• v.5 no.1
• /
• pp.1-15
• /
• 2008

Background: Recent changes in the medical environment have increased the need for the home health care nursing in Korea. Even though the number of home health care patients is increasing, the major nursing problems have not been identified due to lack of a standardized nursing diagnosis. Aim: An investigative study was conducted to determine the frequency and appropriateness of nursing problems in hospital-based home health care patients in Korea using two internationally standardized nursing diagnosis classification systems. Methods: Nursing records of 249 hospital-based home health care patients were reviewed and nursing problems were identified using the North American Nursing Diagnosis Association Nursing Diagnosis Taxonomy I (NANDA) and the Clinical Care Classification of Nursing Diagnoses (CCC). Findings: Out of 463 nursing problems. 403 nursing problems were described using the NANDA whereas 427 nursing problems were described using the CCC. Nursing diagnoses not captured by the NANDA classification include nausea/vomiting, anorexia, risk for nutrition deficit, decreased blood pressure, dying process, blood sugar impairment. infection unspecified, and disuse syndrome. Nursing diagnoses not captured by the CCC include nausea/vomiting and anorexia. Conclusions: In describing nursing problems of home health care patients, it was found that the CCC was able to represent more diagnoses than the NANDA.

• The Journal of the Korean dental association
• /
• v.48 no.6
• /
• pp.459-468
• /
• 2010

International Classification of Disease (ICD-10) is widely used as a crucial reference not only in the medical diagnosis of diseases but also within the health insurance system. It makes possible for medical personnel to make decisions systematically and for the people working in the health insurance or public health industries to better understand medical issues. However, this classification is often not enough or acceptable in a clinical setting. Many countries amend in their own way to make it more appropriate for their people. Korean Classification of Disease (KCD-5) was made by adding a 5 digit code for some diseases to clarify the conditions of the patients. The authors found problems of KCD-5 in temporomandibular disorders and several related medical problems. Medical treatment for these problems had not been covered even by public health insurance until 2000 in Korea. For the last decade, private insurance companies have introduced new items for reimbursement of the treatment fees the patients actually pay. The authors assumed that many patients with these medical problems encountered difficulties in the reimbursement from private insurance companies because KCD-5 did not classify these medical conditions appropriately. An overview of KCD-5 and suggestions for improvement are introduced in this study.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2014.05a
• /
• pp.160-161
• /
• 2014

This paper describes the classification problems of the unit modular housing through reviewing design drawings. Modular construction is a new industrialized homebuilding technology that uses large, factory produced modules. In general, more than 70% of construction processes in unit modular construction method are produced in factory after that the modular, which is completed is lifted on site and assembled. This research considers categorization in which the design drawing of the unit module housing can have problems. There are 17 types of construction method and 14 types of design approach as a result of the classification.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.2
• /
• pp.420-437
• /
• 2024

There are some problems in network traffic classification (NTC), such as complicated statistical features and insufficient training samples, which may cause poor classification effect. A NTC architecture based on one-dimensional Convolutional Neural Network (CNN) and transfer learning is proposed to tackle these problems and improve the fine-grained classification performance. The key points of the proposed architecture include: (1) Model classification--by extracting normalized rate feature set from original data, plus existing statistical features to optimize the CNN NTC model. (2) To apply transfer learning in the classification to improve NTC performance. We collect two typical network flows data from Youku and YouTube, and verify the proposed method through extensive experiments. The results show that compared with existing methods, our method could improve the classification accuracy by around 3-5%for Youku, and by about 7 to 27% for YouTube.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.9 no.6
• /
• pp.1889-1893
• /
• 2008

This paper explores the classification performance of applying to support vector machines (SVMs) for the image classification problems. In this study, we extract the color, texture and shape features of natural images and compare the performance of image classification using each individual feature and integrated features. The experiment results show that classification accuracy on the basis of color feature is better than that based on texture and shape features and the results of the integrating features also provides a better and more robust performance than individual feature. In additions, we show that the proposed classifier of SVM based approach outperforms BPNN to corporate the image classification problems.