• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.56-64
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• 2018

In recent years, imbalanced data is one of the most important and frequent issue for quality control in industrial field. As an example, defect rate has been drastically reduced thanks to highly developed technology and quality management, so that only few defective data can be obtained from production process. Therefore, quality classification should be performed under the condition that one class (defective dataset) is even smaller than the other class (good dataset). However, traditional multi-class classification methods are not appropriate to deal with such an imbalanced dataset, since they classify data from the difference between one class and the others that can hardly be found in imbalanced datasets. Thus, one-class classification that thoroughly learns patterns of target class is more suitable for imbalanced dataset since it only focuses on data in a target class. So far, several one-class classification methods such as one-class support vector machine, neural network and decision tree there have been suggested. One-class support vector machine and neural network can guarantee good classification rate, and decision tree can provide a set of rules that can be clearly interpreted. However, the classifiers obtained from the former two methods consist of complex mathematical functions and cannot be easily understood by users. In case of decision tree, the criterion for rule generation is ambiguous. Therefore, as an alternative, a new one-class classifier using hyper-rectangles was proposed, which performs precise classification compared to other methods and generates rules clearly understood by users as well. In this paper, we suggest an approach for improving the limitations of those previous one-class classification algorithms. Specifically, the suggested approach produces more improved one-class classifier using hyper-rectangles generated by using Gaussian function. The performance of the suggested algorithm is verified by a numerical experiment, which uses several datasets in UCI machine learning repository.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.5
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• pp.497-501
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• 2008

In this paper we present a method for continuous EEG classification, where we employ nonnegative tensor factorization (NTF) to determine discriminative spectral features and use the Viterbi algorithm to continuously classily multiple mental tasks. This is an extension of our previous work on the use of nonnegative matrix factorization (NMF) for EEG classification. Numerical experiments with two data sets in BCI competition, confirm the useful behavior of the method for continuous EEG classification.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.595-601
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• 2003

In this paper we propose an on line training method for classification based on least squares support vector machine. Proposed method enables the computation cost to be reduced and the training to be peformed incrementally, With the incremental formulation of an inverse matrix in optimization problem, current information and new input data can be used for building the new inverse matrix for the estimation of the optimal bias and Lagrange multipliers, so the large scale matrix inversion operation can be avoided. Numerical examples are included which indicate the performance of proposed algorithm.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.965-974
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• 2003

In this paper we propose an incremental classification of multi-class data set by LS-SVM. By encoding the output variable in the training data set appropriately, we obtain a new specific output vectors for the training data sets. Then, online LS-SVM is applied on each newly encoded output vectors. Proposed method will enable the computation cost to be reduced and the training to be performed incrementally. With the incremental formulation of an inverse matrix, the current information and new input data are used for building another new inverse matrix for the estimation of the optimal bias and lagrange multipliers. Computational difficulties of large scale matrix inversion can be avoided. Performance of proposed method are shown via numerical studies and compared with artificial neural network.

• Journal of Korea Society of Industrial Information Systems
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• v.6 no.2
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• pp.1-6
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• 2001

This paper proposes a pattern classification method of interval vectors by interval neural networks. The proposed method can be applied to pattern classification where attribute values of each sample are given as interval numbers. First, an architecture of interval neural networks is proposed for dealing with interval input vectors. Next, a learning algorithm is derived from the cost function. a cost function is defined using the interval output from the interval neural network and the corresponding target output. Last, using numerical examples, the proposed approach is illustrated and compared with other approach based on the standard back-propagation neural networks.

• Proceedings of the Korea Concrete Institute Conference
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• 2004.11a
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• pp.663-666
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• 2004

An analysis of the annual frequency of collapse(AF) is performed for each bridge pier exposed to ship collision. AF is computed for each bridge component and vessel classification. The summation of AFs computed over all of the vessel classification intervals for a specific component should equal the annual frequency of collapse of the component. The designer should use judgment in developing a distribution of the vessel frequency data based on discrete groupings or categories of vessel size by DWT. In the present study the effect of vessel classification on the annual frequency of collapse in the ship collision risk assessment is investigated by illustrative numerical examples based on the vessel frequency data of the domestic harbor. The DWT interval for larger vessels has more effect on the ship collision risk. Therefore the expert judgement in determining the larger DWT interval is required because the design impact lateral resistances of bridge components depend on the ship collision risk.

• Journal of the Korean Society of Safety
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• v.26 no.5
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• pp.33-40
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• 2011

In the hazardous areas where explosive liquids, vapors and gases exist, electrical apparatus/equipment should have explosion-proof construction. The consuming of liquefied natural gas(LNG) has markedly increased in the industrial field, especially in aspect of some thermoprocessing equipment, boiler, dryer, furnace, annealer, kiln, regenerative thermal oxidizer(RTO) and so on. Because it has many merits, clean fuel, safety, no transportation/storage facility and so on. It is strongly recommend that the classification of hazards has to be decided to prevent and protect explosion which may occur in thermoprocessing equipment. In this paper, the operated thermoprocessing equipments in industrial area investigated and explosion risk assessment about LNG leakage from its facilities was performed through numerical calculation and computer simulation. Finally, we suggest the systemic/technical approach for safety assessments of thermoprocessing equipments consumed LNG fuel which are specially subjected to classification of hazardous area.

• Journal of The Korean Association For Science Education
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• v.31 no.5
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• pp.709-719
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• 2011

The purpose of this study was to identify the differences in classification skills shown in classification activities between the gifted and regular students in elementary schools. The subjects for the research consisted of six gifted students in an institute for the gifted for science annexed to P school district in Gangwon-do and 6 students at B and M general elementary schools. Results were as follows: The time taken for classification activities of the gifted was shorter than regular regardless of subjects for classifying. The number of standards for classifying for the gifted was more than regular students. Coefficient for measuring classification skills of the gifted was higher than regulars regardless of age. Consequently, there was a difference in the time taken for classifying and generating the number of standards and in a numerical index of classification activities performed at science classes between the science gifted and the regular students.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2004.05a
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• pp.222-223
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• 2004

• Proceedings of the KWS Conference
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• 2005.06a
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• pp.279-281
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• 2005