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- 저자 : ( Hee Jae Leea ) (검색결과 2 건)
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Effect of nicotine on orthodontic tooth movement and bone remodeling in rats
Sung-Hee Leea,Jung-Yul Chab,Sung-Hwan Choib,Baek-il Kimc,Jae-Kook Chad,Chung-Ju Hwang 대한치과교정학회 2021 대한치과교정학회지 Vol.51 No.4
Objective: To quantitatively analyze the effect of nicotine on orthodontic tooth movement (OTM) and bone remodeling in rats using micro-computed tomography and tartrate-resistant acid phosphatase immunostaining. Methods: Thirty-nine adult male Sprague-Dawley rats were randomized into three groups: group A, 0.5 mL normal saline (n = 9, 3 per 3, 7, and 14 days); group B, 0.83 mg/kg nicotine (n = 15, 5 per 3, 7, and 14 days); and group C, 1.67 mg/kg nicotine (n = 15, 5 per 3, 7, and 14 days). Each animal received daily intraperitoneal injections of nicotine/saline from the day of insertion of identical 30-g orthodontic force delivery systems. A 5-mm nickel-titanium closed-coil spring was applied between the left maxillary first molar (M1) and the two splinted incisors. The rate of OTM and volumetric bone changes were measured using micro-computed tomography. Osteoclasts were counted on the mesial alveolar bone surface of the distobuccal root of M1. Six dependent outcome variables, including the intermolar distance, bone volume fraction, bone mineral density, trabecular thickness, trabecular volume, and osteoclast number, were summarized using simple descriptive statistics. Nonparametric Kruskal-Wallis tests were used to evaluate differences among groups at 3, 7, and 14 days of OTM. Results: All six dependent outcome variables showed no statistically significant among group-differences at 3, 7, and 14 days. Conclusions: The findings of this study suggest that nicotine does not affect OTM and bone remodeling, although fluctuations during the different stages of OTM in the nicotine groups should be elucidated in further prospective studies. [Korean J Orthod 2021;51(4):282-292]
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데이터 전처리와 앙상블 기법을 통한 불균형 데이터의 분류모형 비교 연구
이희재,이성임,Leea, Hee-Jae,Lee, Sungim 한국통계학회 2014 응용통계연구 Vol.27 No.3
There are many studies related to imbalanced data in which the class distribution is highly skewed. To address the problem of imbalanced data, previous studies deal with resampling techniques which correct the skewness of the class distribution in each sampled subset by using under-sampling, over-sampling or hybrid-sampling such as SMOTE. Ensemble methods have also alleviated the problem of class imbalanced data. In this paper, we compare around a dozen algorithms that combine the ensemble methods and resampling techniques based on simulated data sets generated by the Backbone model, which can handle the imbalance rate. The results on various real imbalanced data sets are also presented to compare the effectiveness of algorithms. As a result, we highly recommend the resampling technique combining ensemble methods for imbalanced data in which the proportion of the minority class is less than 10%. We also find that each ensemble method has a well-matched sampling technique. The algorithms which combine bagging or random forest ensembles with random undersampling tend to perform well; however, the boosting ensemble appears to perform better with over-sampling. All ensemble methods combined with SMOTE outperform in most situations. 최근 들어 데이터 마이닝의 분류문제에 있어 목표변수의 불균형 문제가 많은 관심을 받고 있다. 이러한 문제를 해결하기 위해, 이전 연구들은 원 자료에 대하여 데이터 전처리 과정을 실시했는데, 전처리 과정에는 목표변수의 다수계급을 소수계급의 비율에 맞게 조정하는 과소표집법, 소수계급을 복원추출하여 다수계급의 비율에 맞게 조정하는 과대표집법, 소수계급에 K-최근접 이웃 방법 등을 활용하여 과대표집법을 적용 후 다수계급에는 과소표집법을 적용한 하이브리드 기법 등이 있다. 또한 앙상블 기법도 이러한 불균형 데이터의 분류 성능을 높일 수 있다고 알려져 있어, 본 논문에서는 데이터의 전처리 과정과 앙상블 기법을 함께 고려한 여러 모형들을 사용하여, 불균형 자료에 대한 이들모형의 분류성능을 비교평가한다.
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