• Communications for Statistical Applications and Methods
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• v.20 no.3
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• pp.225-233
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• 2013

The linear support vector machine(SVM) is motivated by the maximal margin separating hyperplane and is a popular tool for binary classification tasks. Many studies exist on the consistency properties of SVM; however, it is unknown whether the linear SVM is consistent for estimating the optimal classification boundary even in the simple case of two Gaussian classes with a common covariance, where the optimal classification boundary is linear. In this paper we show that the linear SVM can be inconsistent in the univariate Gaussian classification problem with a common variance, even when the best tuning parameter is used.

• Journal of Sasang Constitutional Medicine
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• v.18 no.2
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• pp.96-112
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• 2006

1. Objectives This study focuses on the reliability of the Questionnaire about Sasang Constitution Classification for Mongolians 2. Methods Test-retest method and internal consistency method have been performed based on the absolutely diagnosed group of 87 cases who respond to the questionaries during the time interval of one yeat between July 2003 and July 2004 to verify the confidence level. 3. Results and Conclusions (1) In the test-retest for each question of the Questionnaire of Sasang Constitution Classification for Mongolians, the dependency ratio is 40% and the agreement ratio is 92%. Therefore, this questionnaire has credibility because it has question relations and high agreement ratio. (2) In the internal consistency method for the measure of the Questionnaire of Sasang Constitution Classification for Mongolians, the value of Cronbach alpha is mote than 0.60. As a result, this questionnaire has internal consistency for each question which explains each physical constitution and it has credibility (3) In the internal consistency method for the measure of the Questionnaire of Sasang Constitution Classification for Mongolians, the Pearson's correlation coefficient, r, falls between $+0.38\;{\sim}\;+0.54$ in each measure. Accordingly, this questionnaire has internal consistency between each physical constitution measure and it has credibility.

• Journal of Gastric Cancer
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• v.11 no.2
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• pp.78-85
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• 2011

Since January of 2010, the seventh edition of UICC tumor node metastasis (TNM) Classification, which has recently been revised, has been applied to almost all cases of malignant tumors. Compared to previous editions, the merits and demerits of the current revisions were analyzed. Many revisions have been made for criteria for the classification of lymph nodes. In particular, all the cases in whom the number of lymph nodes is more than 7 were classified as N3 without being differentiated. Therefore, the coverage of the N3 was broad. Owing to this, there was no consistency in predicting the prognosis of the N3 group. By determining the positive cases to a distant metastasis as TNM stage IV, the discrepancy in the TNM stage IV compared to the sixth edition was resolved. In regard to the classification system for an esophagogastric (EG) junction carcinoma, it was declared that cases of an invasion to the EG junction should follow the classification system for esophageal cancer. A review of clinical cases reported from Asian patients suggests that it would be more appropriate to follow the previous editions of the classification system for gastric cancer. In addition, in the classification of the TNM stages in the overall cases, the discrepancy in the prognosis between the different stages and the consistency in the prognosis between the same TNM stages were achieved to a lesser extent as compared to that previously. Accordingly, further revisions are needed to develop a purposive classification method where the prognosis can be predicted specifically to each variable and the mode of the overall classification can be simplified.

• Communications for Statistical Applications and Methods
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• v.26 no.6
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• pp.635-644
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• 2019

Class probability is a fundamental target in classification that contains complete classification information. In this article, we propose a class probability estimation method when the predictor is functional. Motivated by Wang et al. (Biometrika, 95, 149-167, 2007), our estimator is obtained by training a sequence of functional weighted support vector machines (FWSVM) with different weights, which can be justified by the Fisher consistency of the hinge loss. The proposed method can be extended to multiclass classification via pairwise coupling proposed by Wu et al. (Journal of Machine Learning Research, 5, 975-1005, 2004). The use of FWSVM makes our method model-free as well as computationally efficient due to the piecewise linearity of the FWSVM solutions as functions of the weight. Numerical investigation to both synthetic and real data show the advantageous performance of the proposed method.

• Science of Emotion and Sensibility
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• v.26 no.3
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• pp.15-28
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• 2023

This study examined the affective representation and response consistency among individuals using affective videos, a naturalistic stimulus inducing emotional experiences most similar to those in daily life. In this study, multidimensional scaling was conducted to investigate whether the various affective representations induced through video stimuli are located in the core affect dimensions. A cross-participant classification analysis was also performed to verify whether the video stimuli are well classified. Additionally, the newly developed intersubject correlation analysis was conducted to assess the consistency of affective representations across participant responses. Multidimensional scaling revealed that the video stimuli are represented well in the valence dimension, partially supporting Russell (1980)'s core affect theory. The classification results showed that affective conditions were successfully classified across participant responses. Moreover, the intersubject correlation analysis showed that the consistency of affective representations to video stimuli differed with respect to the condition. This study suggests that the affective representations and consistency of individual responses to affective videos varied across different affective conditions.

• Journal of the Korean Society for information Management
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• v.18 no.4
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• pp.201-215
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• 2001

The purpose of this study is to identify how to construct an internet shopping mall classification system used on the library classification theories. To aid in identifying classification system, this study focused on the Ranganathan’s classification canons; canons for characteristics, canons for terms. The study shows six priniciples for an internet shopping mall classification system construct: products’characteristics, inclusiveness, various access points, category sequence and term consistency, term currency and obviousness, no term duplication. For future research, product’s search patterns and relationship to interface are suggested.

• Korean journal of medical education
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• v.30 no.4
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• pp.347-357
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• 2018

Purpose: The purpose of this study was to apply alternative standard setting methods for the Korean Medical Licensing Examination (KMLE), a criterion-referenced written examination, and to compare them to the conventional cut score used on the KMLE. Methods: The process and results of criterion-referenced standard settings (i.e., the modified-Angoff and bookmark methods) were evaluated. The ratio of passing and failing examinees determined using these alternative standard setting methods was compared to the results of the conventional criteria. Additionally, the external, internal and procedural evaluation of these methods were reviewed. Results: The modified-Angoff method yielded the highest cut score, followed sequentially by the conventional method and the bookmark method. The classification agreement between the modified-Angoff and bookmark methods was 0.720 measured by Cohen's ${\kappa}$ coefficient. The intra-panelist classification consistency of modified-Angoff method was higher than bookmark method. However, the inter-panelist classification consistency was vice versa. The standard setting panelists' survey results showed that the procedures of both methods were satisfactory, but panelists had more confidence in the results of the modified-Angoff method. Conclusion: The modified-Angoff method showed results that were more similar to those of the conventional method. Both new methods showed very high concordance with the conventional method, as well as with each other. The modified-Angoff method was considered feasible for adoption on the KMLE. The standard setting panelists responded positively to the modified-Angoff method in terms of its practical applicability, despite certain advantages of the bookmark method.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.672-674
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• 2000

There are various ways in classification methodologies of data mining such as neural networks but the result should be explicit and understandable and the classification rules be short and clear. Rough set theory is a effective technique in extracting knowledge from incomplete and inconsistent information and makes an offer classification and approximation by various attributes with effect. This paper discusses granularity of knowledge for reasoning of uncertain concepts by using generalized rough set approximations based on hierarchical granulation structure and uses hierarchical classification methodology that is more effective technique for classification by applying core to upper level. The consistency rules with minimal attributes is discovered and applied to classifying real data.

• Steel and Composite Structures
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• v.7 no.3
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• pp.185-200
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• 2007

Tubular construction is widely used in a range of civil and structural engineering applications. To date, the principal product range has comprised square, rectangular and circular hollow sections. However, hot-rolled structural steel elliptical hollow sections have been recently introduced and offer further choice to engineers and architects. Currently though, a lack of fundamental structural performance data and verified structural design guidance is inhibiting uptake. Of fundamental importance to structural metallic design is the concept of cross-section classification. This paper proposes slenderness parameters and a system of cross-section classification limits for elliptical hollow sections, developed on the basis of laboratory tests and numerical simulations. Four classes of cross-sections, namely Class 1 to 4 have been defined with limiting slenderness values. For the special case of elliptical hollow sections with an aspect ratio of unity, consistency with the slenderness limits for circular hollow sections in Eurocode 3 has been achieved. The proposed system of cross-section classification underpins the development of further design guidance for elliptical hollow sections.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1771-1777
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• 2023

With the advancements of deep learning, many semantic segmentation-based methods for land cover classification have been proposed. However, existing deep learning-based models only use image information and cannot guarantee spatiotemporal consistency. In this study, we propose a land cover classification model using geographical coordinates. First, the coordinate features are extracted through the Coordinate Hash Encoder, which is an extension of the Multi-resolution Hash Encoder, an implicit neural representation technique, to the longitude-latitude coordinate system. Next, we propose an architecture that combines the extracted coordinate features with different levels of U-net decoder. Experimental results show that the proposed method improves the mean intersection over union by about 32% and improves the spatiotemporal consistency.