• Journal of the Korean Statistical Society
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• v.30 no.2
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• pp.247-263
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• 2001

This paper concerns a problem of classification based on training dta. A framework of information geometry is given to elucidate the characteristics of discriminant functions including logistic discrimination and AdaBoost. We discuss a class of loss functions from a unified viewpoint.

• Structural Engineering and Mechanics
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• v.51 no.6
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• pp.973-988
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• 2014

This paper presents a factor-analysis based questionnaire categorization method to improve the reliability of the evaluation of working conditions without influencing the completeness of the questionnaire both in Taiwanese and Chinese construction enterprises for structural engineering applications. The proposed approach springs from the AI application and expert systems in structural engineering. Questions with a similar response pattern are grouped into or categorized as one factor. Questions that form a single factor usually have higher reliability than the entire questionnaire, especially in the case when the questionnaire is complex and inconsistent. By classifying questions based on the meanings of the words used in them and the responded scores, reliability could be increased. The principle for classification was that 90% of the questions in the same classified group must satisfy the proposed classification rule and consequently the lowest one was 92%. The results show that the question classification method could improve the reliability of the questionnaires for at least 0.7. Compared to the question deletion method using SPSS, 75% of the questions left were verified the same as the results obtained by applying the classification method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.576-579
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• 2009

The amount of incoming e-mails is increasing rapidly due to the wide usage of Internet. Therefore, it is more required to classify incoming e-mails efficiently and accurately. Currently, the e-mail classification techniques are focused on two way classification to filter spam mails from normal ones based mainly on Bayesian and Rule. The clustering method has been used for the multi-way classification of e-mails. But it has a disadvantage of low accuracy of classification. In this paper, we propose a novel multi-way e-mail classification method that uses PCA for automatic category generation and dynamic category hierarchy for high accuracy of classification. It classifies a huge amount of incoming e-mails automatically, efficiently, and accurately.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.931-940
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• 2011

Classification is to generate a rule of classifying objects into several categories based on the learning sample. Good classification model should classify new objects with low misclassification error. Many types of classification methods have been developed including logistic regression, discriminant analysis and tree. This paper presents a new classification method using penalized partial least squares. Penalized partial least squares can make the model more robust and remedy multicollinearity problem. This paper compares the proposed method with logistic regression and PCA based discriminant analysis by some real and artificial data. It is concluded that the new method has better power as compared with other methods.

• The Journal of the Acoustical Society of Korea
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• v.16 no.1E
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• pp.64-74
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• 1997

In many decision making systems, rule-based approaches are used to solve complex problems in the areas of pattern analysis and computer vision. In this paper, we present methods for generating fuzzy IF-THEN rules automatically from training data for pattern classification and high-level computer vision. The rules are generated by construction minimal approximate fuzzy aggregation networks and then training the networks using gradient descent methods. The training data that represent features are treated as linguistic variables that appear in the antecedent clauses of the rules. Methods to generate the corresponding linguistic labels(values) and their membership functions are presented. In addition, an inference procedure is employed to deduce conclusions from information presented to our rule-base. Two experimental results involving synthetic and real are given.

• Phonetics and Speech Sciences
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• v.1 no.3
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• pp.155-163
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• 2009

This paper introduces an automatic voice classification system for the diagnosis of individual constitution based on Sasang Constitutional Medicine (SCM) in Traditional Korean Medicine (TKM). For the developing of this algorithm, we used the voices of 473 speakers and extracted a total of 144 speech features from the speech data consisting of five sustained vowels and one sentence. The classification system, based on a rule-based algorithm that is derived from a non parametric statistical method, presents binary negative decisions. In conclusion, 55.7% of the speech data were diagnosed by this system, of which 72.8% were correct negative decisions.

• Journal of Biomedical Engineering Research
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• v.42 no.6
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• pp.277-286
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• 2021

The global market size of AI based SaMD for medical image in 2023 will be anticipated to reach around 620 billion won (518 million dollars). In order for Korean manufacturers to efficiently obtain CE marking for marketing in the EU countries, the paper is to introduce the recommendation and suggestion of how to reclassify SaMD based on classification rules of MDR because, after introducing the Regulation EU 2017/745, classification rules are quite modified and newly added compared to the Directive 93/42/EEC. In addition, the paper is to provide several rules of MDR that may be applicable to decide the classification of SaMD. Lastly, the paper is to examine and demonstrate various secondary data supported by qualitative data because the paper focuses on the suggestion and recommendation with a public trust on the basis of various secondary data conducted by the analysis of field data. In conclusion, the paper found that the previous classification of SaMD followed by the rule of MDD should be reclassified based on the Regulation EU 2017/745. Therefore, the suggestion and recommendation are useful for Korean manufacturers to comprehend the classification of SaMD for marketing in the EU countries.

• The Journal of the Acoustical Society of Korea
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• v.28 no.5
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• pp.471-476
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• 2009

In this paper, we apply a discriminative weight training to a support vector machine (SVM) based speech/music classification for the selectable mode vocoder (SMV) of 3GPP2. In our approach, the speech/music decision rule is expressed as the SVM discriminant function by incorporating optimally weighted features of the SMV based on a minimum classification error (MCE) method which is different from the previous work in that different weights are assigned to each the feature of SMV. The performance of the proposed approach is evaluated under various conditions and yields better results compared with the conventional scheme in the SVM.

• Journal of Digital Contents Society
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• v.15 no.4
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• pp.559-568
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• 2014

Previous polarity classification using sentiment analysis utilizes a sentence rule by product reviews based rating points. It is difficult to be applied to blogs which have not rating of product reviews and is possible to fabricate product reviews by comment part-timers and managers who use web site so it is not easy to understand a product and store reviews which are reliability. Considering to these problems, if we analyze blogs which have personal and frank opinions and classify polarity, it is possible to understand rightly opinions for the product, store. This paper suggests that we extract high frequency vocabularies in blogs by several domains and choose topic words. Then we apply a technique of sentiment analysis and classify polarity about contents of blogs. To evaluate performances of sentiment analysis, we utilize the measurement index that use Precision, Recall, F-Score in an information retrieval field. In a result of evaluation, using suggested sentiment analysis is the better performances to classify polarity than previous techniques of using the sentence rule based product reviews.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.83-93
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• 2001

Prediction of corporate failure using past financial data is well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks (NNs) can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. Although numerous theoretical and experimental studies reported the usefulness or neural networks in classification studies, there exists a major drawback in building and using the model. That is, the user can not readily comprehend the final rules that the neural network models acquire. We propose a genetic algorithms (GAs) approach in this study and illustrate how GAs can be applied to corporate failure prediction modeling. An advantage of GAs approach offers is that it is capable of extracting rules that are easy to understand for users like expert systems. The preliminary results show that rule extraction approach using GAs for bankruptcy prediction modeling is promising.