• Journal of Sasang Constitutional Medicine
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• v.19 no.1
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• pp.98-115
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• 2007

1. Objectives This study focuses on the Validity of the Questionnaire about Sasang Constitution Classification for Mongolians 2. Methods By using the way of backward elimination, certain variables are chosen from the 438 cases whose physical conditions are absolutely diagnosed. After that, discriminant analysis for the selected variables has been done to obtain the physical constitution equation and the accuracy ratio of diagnosis which are useful for physical constitution diagnosis. 3. Results and Conclusions (1) In tile Validity for the Questionnaire of Sasang Constitution Classification for Mongolians, the accuracy ratio of diagnosis of Taeyangin is 100%, Soyangin 62.5%, Taeumin 76.7%, and Soeumin 66.1% respectively as a result of the discriminant analysis employing Cronbach's alpha coefficient. On the whole, the accuracy ratio of diagnosis is 70.1%. (2). In the Validity for the Questionnaire of Sasang Constitution Classification for Mongolians, the accuracy ratio of diagnosis of 70.1% means that it beats the maximum chance criterion of 41.4% and the proportional chance criterion of 34.4% by 28.7% and 35.7% respectively. Conclusively, this questionnaire has discriminant power.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.5
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• pp.457-464
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• 2009

The weak classifier of AdaBoost algorithm is a central classification element that uses a single criterion separating positive and negative learning candidates. Finding the best criterion to separate two feature distributions influences learning capacity of the algorithm. A common way to classify the distributions is to use the mean value of the features. However, positive and negative distributions of Haar-like feature as an image descriptor are hard to classify by a single threshold. The poor classification ability of the single threshold also increases the number of boosting operations, and finally results in a poor classifier. This paper proposes a weak classifier that uses multiple criterions by adding a probabilistic criterion of the positive candidate distribution with the conventional mean classifier: the positive distribution has low variation and the values are closer to the mean while the negative distribution has large variation and values are widely spread. The difference in the variance for the positive and negative distributions is used as an additional criterion. In the learning procedure, we use a new classifier that provides a better classifier between them by selective switching between the mean and standard deviation. We call this new type of combined classifier the "Mixed Weak Classifier". The proposed weak classifier is more robust than the mean classifier alone and decreases the number of boosting operations to be converged.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.32 no.3
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• pp.209-220
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• 2022

Objectives: This study was performed to suggest how to re-establish criterion for toxic substances under the Chemical Control Act (CCA) in South Korea by comparing the GHS (Globally Harmonized System of Classification and Labeling of Chemicals) score and toxic properties. Methods: Toxic substances were classified into seven groups (Acute toxicity (1A), Chronic toxicity (2C), Environmental hazards (3E), Acute toxicity & chronic toxicity (4AC), Chronic toxicity & environmental hazards (5CE), Acute toxicity & environmental hazards (6AE), and Acute toxicity & chronic toxicity & environmental hazards (7ACE)) according to their toxic properties. The GHS score was calculated to sum up five toxicity indicators (health acute toxicity, health repeated toxicity, carcinogenicity, health other chronic toxicity and environmental hazards). Results: The GHS score of 7ACE was higher by 7 times that of 1A. 1A is the only group which has lower than the total GHS score. The highest score was 47, for sodium chromate (CAS no. 7775-11-3), which belongs to group 7ACE. This is classified as acute toxicity, carcinogenicity, germ cell mutagenicity, reproductive toxicity, and acute and chronic environmental hazard. On the other hand, the lowest score was 2.75, which was assigned to 177 chemicals belonging to group 1A. When the health acute toxicity indicator was omitted from the toxic criterion, toxic substances could be divided into the sub-groups 'human chronic hazards group' (HCG) and 'environmental hazards group' (EG) according to their GHS score and properties. Conclusions: The proposed criterion for toxic substances is to establish sub-groups defined as HCG and EG for separate control and that the 1A group be moved to substances requiring preparation for accidents under the CCA.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.2B
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• pp.131-137
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• 2012

River areas are classified as conservation, restoration, and recreation zones depending on engineers' opinions from their experiences at present. For conservation zones, almost all engineers have the same opinions because natural characteristics are considered for classification. However, it is difficult to decide a basis in classifying restoration and recreation zones in mixed areas by urban and rural streams. This study attempted to prove an application of a previous study (Song & Yoon, 2008) that suggested two classification techniques to classify conservation or maintenance zones, and reclassify maintenance zones into restoration or recreation zones. The suggested classification techniques of river zones were used to estimate 46 reaches of 20 urban streams, 47 reaches of 29 rural streams, and 48 reaches of 19 mountainous streams to achieve a purpose of this study. The conservation, restoration, and recreation zones were reasonably divided by results of the suggested techniques. A possibility that quantified criterion could be used to classify river zones was proven in this study.

• Journal of information and communication convergence engineering
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• v.5 no.3
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• pp.245-248
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• 2007

In this paper, we research the content-based analysis and classification according to the composition of the feature parameter data base for the audio data to implement the audio data index and searching system. Audio data is classified to the primitive various auditory types. We described the analysis and feature extraction method for the feature parameters available to the audio data classification. And we compose the feature parameters data base in the index group unit, then compare and analyze the audio data centering the including level around and index criterion into the audio categories. Based on this result, we compose feature vectors of audio data according to the classification categories, and simulate to classify using discrimination function.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.627-635
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• 2003

In this short communication we discuss the bias problem of C4.5 in split variable selection and suggest a method to reduce the variable selection bias among categorical predictor variables. A penalty proportional to the number of categories is applied to the splitting criterion gain of C4.5. The results of empirical comparisons show that the proposed modification of C4.5 reduces the size of classification trees.

• International Journal of Highway Engineering
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• v.7 no.4 s.26
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• pp.57-68
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• 2005

Vehicle classification system in Korea is operated by two different types depending on operating purpose and place. 8-category classification system operates in Expressway and Provincial road, and 11-category classification system operates in National highway. These different operations decrease the efficiency of practical use of gathering data. Therefore, this study proposes new-modified vehicle classification system for solving this problem. For classification, this study not only focuses on mechanic survey system which is based on vehicle specs, it's also focuses on the applicability of roadside survey. This proposed classification system considers the tendency to vary of vehicle types, and the compatibility with the other classification systems. This system might be the most suitable system for our present situation.

• Journal of the Korean Society for Library and Information Science
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• v.49 no.1
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• pp.319-340
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• 2015

The purpose of this study is to provide a plan of classification scheme to efficiently manage the collected cultural resource in Asian Culture Information Agency (ACIA) of Asian Culture Complex. The characteristic and category of the cultural resources are identified after studying objectives and acquisition policies of ACIA. This paper in here compares classification schemes such as HRAF scheme, UNESCO cultural framework, Folklore Archive scheme, and classification scheme of Academy of Korean Studies. On the basis of it, this study proposes the principle and criterion of the new classification scheme in ACIA. The new classification scheme is classified as the cultural, social, and natural area in sequence. The number of main classes is set up 16 items.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.421-427
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• 2001

In this paper, a method for selection of the optimal feature vectors is proposed for the classification of closed 2D shapes using the bispectrum of a contour sequence. The bispectrum based on third order cumulants is applied to the contour sequences of the images to extract feature vectors for each planar image. These bispectral feature vectors, which are invariant to shape translation, rotation and scale transformation, can be used to represent two-dimensional planar images, but there is no certain criterion on the selection of the feature vectors for optimal classification of closed 2D images. In this paper, a new method for selecting the optimal bispectral feature vectors based on the variances of the feature vectors. The experimental results are presented using eight different shapes of aircraft images, the feature vectors of the bispectrum from five to fifteen and an weighted mean fuzzy classifier.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.209-212
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• 2005

Hyperspectral data consists of more than 200 spectral bands that are highly correlated. In order to utilize hyperspectral data for classification, dimensional reduction or feature extraction is desired. By applying feature extraction, computational complexity of classification can be reduced and classification accuracy may be improved. In this paper, a genetic algorithm based feature extraction technique is proposed. Measure from discriminant analysis is utilized as optimization criterion. A subset of spectral bands is selected by genetic algorithm. Dimension of feature space is further reduced by linear transformation. Feasibility of the proposed technique is evaluated with AVIRIS data.