• Journal of Digital Convergence
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• v.17 no.6
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• pp.389-399
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• 2019

The purpose of this study was to investigate the effects of muscle asymmetry of knee joint among elite cyclists on anaerobic pedaling power related capacity. In another word, based on isokinetic strength of Non-Dominant, ND and Dominant, D, side, high, moderate and low ratio of ND to D were classified as High Symmetry Group, Moderate Symmetry Group and Asymmetry Group, respectively. Analysis of muscle asymmetry of extensor's ND and D side might not lead to any difference between the three groups. Based on muscle strength analysis of the flexor's ND and D, there was statistical difference between the groups in ND flexor and in the muscle balance index of the flexor muscle. This result also leads to significant difference in pedaling power functionality, but this effects might not lead to any negative pedaling power. Therefore, among even cyclists who may show almost the same recruitment pattern of ND and D side during pedaling stroke muscle asymmetry could exist but this phenomena might not negatively contribute to the pedaling capacity.

• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.9-21
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• 2015

The support vector machine has been successfully applied to various classification areas due to its flexibility and a high level of classification accuracy. However, when analyzing imbalanced data with uneven class sizes, the classification accuracy of SVM may drop significantly in predicting minority class because the SVM classifiers are undesirably biased toward the majority class. The weighted $L_2$-norm SVM was developed for the analysis of imbalanced data; however, it cannot identify irrelevant input variables due to the characteristics of the ridge penalty. Therefore, we propose the weighted $L_1$-norm SVM, which uses lasso penalty to select important input variables and weights to differentiate the misclassification of data points between classes. We demonstrate the satisfactory performance of the proposed method through simulation studies and a real data analysis.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.177-188
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• 2022

The support vector machine (SVM) has been successfully applied to various classification areas with a high level of classification accuracy. However, it is infeasible to use the SVM in analyzing massive data because of its significant computational problems. When analyzing imbalanced data with different class sizes, furthermore, the classification accuracy of SVM in minority class may drop significantly because its classifier could be biased toward the majority class. To overcome such a problem, we propose the DOC-SVM method, which uses divide-oversampling and conquers techniques. The proposed DOC-SVM divides the majority class into a few subsets and applies an oversampling technique to the minority class in order to produce the balanced subsets. And then the DOC-SVM obtains the final classifier by aggregating all SVM classifiers obtained from the balanced subsets. Simulation studies are presented to demonstrate the satisfactory performance of the proposed method.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.165-171
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• 2000

일원분류 모형에서 표준 F-검정을 하기 위해서는 오차항에 대한 등분산성을 가정한다. 그러나 실제로 이러한 가정은 지켜지기 힘들며, 이에 더불어 관찰치가 각 집단별로 일정하지 않고 불균형한 경우에는 F-검정의 유의수준이 지정된 값을 만족시키지 못하며, 따라서 검정력에 관한 분석은 의미가 없게 된다. 본 연구에서는 등분산성이 지켜지지 않고, 자료가 불균형한 경우, 현실적인 상황에서 일반적으로 사용되는 F-검정의 유의수준 유지라는 문제가 어 떤 변화를 겪게 되는지를 확인하고, 더 나아가 유의수준을 유지하기 위해서는 어떤 식의 조정이 가능한지를 살펴보았다.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.495-509
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• 2015

We study a classification problem of significant differences in the proportion of two groups known as the unbalanced classification problem. It is usually more difficult to classify classes accurately in unbalanced data than balanced data. Most observations are likely to be classified to the bigger group if we apply classification methods to the unbalanced data because it can minimize the misclassification loss. However, this smaller group is misclassified as the larger group problem that can cause a bigger loss in most real applications. We compare several classification methods for the unbalanced data using sampling techniques (up and down sampling). We also check the total loss of different classification methods when the asymmetric loss is applied to simulated and real data. We use the misclassification rate, G-mean, ROC and AUC (area under the curve) for the performance comparison.

• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.411-420
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• 2005

In this study, the MIVQUE estimators of variance components for the one-way random model with unbalanced data are investigated. In order to compare the efficiency of MIVQUE estimators obtained by using three priori estimates, the Empirical Quantile Dispersion Graphs (EQDGs) are used. From the results of Monte-Carlo study, the MIVQUE estimator using ${\sigma}^2_{\alpha}\;=\;0\;and\;{\sigma}^2_{varraho}=1$ as the priori estimate performs well relative to other estimators.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.961-975
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• 2016

The hierarchically penalized support vector machine (H-SVM) has been developed to perform simultaneous classification and input variable selection when input variables are naturally grouped or generated by factors. However, the H-SVM may suffer from estimation inefficiency because it applies the same amount of shrinkage to each variable without assessing its relative importance. In addition, when analyzing imbalanced data with uneven class sizes, the classification accuracy of the H-SVM may drop significantly in predicting minority class because its classifiers are undesirably biased toward the majority class. To remedy such problems, we propose the weighted adaptive H-SVM (WAH-SVM) method, which uses a adaptive tuning parameters to improve the performance of variable selection and the weights to differentiate the misclassification of data points between classes. Numerical results are presented to demonstrate the competitive performance of the proposed WAH-SVM over existing SVM methods.

• Journal of Digital Convergence
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• v.17 no.3
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• pp.463-471
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• 2019

The purpose of this study was to investigate the effect of muscle imbalance on motion of back squat. The isokinetic muscle strength of the 8 subjects was recorded for the knee flexion/extension by the cybex 770 dynamometer. Each subject performed 3 back squats with the long barbell with an intensity of 25% body weight(BW), 50%BW, 100%BW, 125%BW. During the back squat through the recorded kinematic data the subjects' maximum flexion and extension knee angle, center of mass displacement and V-COP were calculated for evaluation of the stability of the movement. For the statistical analysis independent t-test was used. Knee flexion angle and COM displacement are dominated by the reciprocal muscle ratio. V-COP factor was dominated by bilateral extension deficit. Based on the results we can know that as the intensity of the squat increased to a level control was difficult because the muscles' imbalance influenced the movement.

• Journal of Distribution Research
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• v.10 no.2
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• pp.49-72
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• 2005

As interdependence and conflict are important to the understanding of channel interactions, many researchers have studied their relationship. Identifying the relationship between interdependence and conflict will help understanding an exchange relationship. In social science, the relationship between interdependence and conflict is explained by two contradictory theories, and there are also inconsistent results in marketing science. The authors suggest non-linear relations between asymmetric interdependence and conflict, based on bilateral deterrence theory and conflict spiral theory. Using survey data from industrial market, we demonstrate that there is an inverted U-shaped relationship between asymmetric interdependence and interfirm conflict. The result show, as the magnitude of interdependence is high. the hypothesis on the non-linear relationship between asymmetric interdependence and conflicts is acceptable on both suppliers and distributors. Finally, we discuss several theoretical implications and suggest limitations and future research issues.

• The Korean Journal of Applied Statistics
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• v.37 no.4
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• pp.395-410
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• 2024

Text data is usually made up of a wide variety of unique words. Even in standard text data, it is common to find tens of thousands of different words. In text data analysis, usually, each unique word is treated as a variable. Thus, text data can be regarded as a dataset with a large number of variables. On the other hand, in text data classification, we often encounter class label imbalance problems. In the cases of substantial imbalances, the performance of conventional classification models can be severely degraded. To improve the classification performance of support vector machines (SVM) for imbalanced data, algorithms such as the Synthetic Minority Over-sampling Technique (SMOTE) can be used. The SMOTE algorithm synthetically generates new observations for the minority class based on the k-Nearest Neighbors (kNN) algorithm. However, in datasets with a large number of variables, such as text data, errors may accumulate. This can potentially impact the performance of the kNN algorithm. In this study, we propose a method for enhancing prediction performance for the minority class of imbalanced text data. Our approach involves employing variable selection to generate new synthetic observations in a reduced space, thereby improving the overall classification performance of SVM.