• Journal of Information Processing Systems
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• v.17 no.1
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• pp.89-106
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• 2021

Different samples can have different effects on learning support vector machine (SVM) classifiers. To rebalance an imbalanced dataset, it is reasonable to reduce non-informative samples and add informative samples for learning classifiers. Safe sample screening can identify a part of non-informative samples and retain informative samples. This study developed a resampling algorithm for Rebalancing imbalanced data using Safe Sample Screening (Re-SSS), which is composed of selecting Informative Samples (Re-SSS-IS) and rebalancing via a Weighted SMOTE (Re-SSS-WSMOTE). The Re-SSS-IS selects informative samples from the majority class, and determines a suitable regularization parameter for SVM, while the Re-SSS-WSMOTE generates informative minority samples. Both Re-SSS-IS and Re-SSS-WSMOTE are based on safe sampling screening. The experimental results show that Re-SSS can effectively improve the classification performance of imbalanced classification problems.

• Journal of Applied Reliability
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• v.2 no.2
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• pp.121-133
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• 2002

This paper concerns informative censoring and some of the difficulties it creates in analysis of survival data. For analyzing censored data, misclassification of informative censoring into random censoring is often unavoidable. It is worthwhile to investigate the impact of neglecting informative censoring on the estimation of the parameters of the proportional hazards model. The proposed model includes a primary failure which can be censored informatively or randomly and a followup failure which may be censored randomly. Simulation shows that the loss is about 30% with regard to the confidence interval if we neglect the informative censoring.

• Journal of the Korean Graphic Arts Communication Society
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• v.30 no.2
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• pp.13-22
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• 2012

This research attempts to analyze communication effects of print ad having pictorial typography. 150 Questionnaires were distributed to respondents staying Daejeun City and 148 copies were retreated for five days from April 22nd to 26th, 2012. Frequency analysis, factor analysis, Cronbach's alpha for reliability analysis were utilized for data analysis with SPSS 12.0. For testing hypothesis, regression analysis was used. As result of testing hypothesis, 'informative, beneficial, creative, reliable' were partially significant to attitude towards print ad having pictorial typography. That means 'creative' and 'reliable' were insignificant, while 'informative' and 'beneficial' are significant. Variable of the most influencing on attitude towards advertising is 'informative.' 'Informative, beneficial, creative, and reliable' were partially significant to brand attitude, too. That means 'beneficial' and 'creative' were insignificant, while 'informative' and 'reliable' were significant. Variable of the most influencing on brand attitude was 'reliable.' Therefore, to enhance communication effect of print ad having pictorial typography, 'informative' and 'reliable' are most significant variables.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.657-666
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• 2022

An informative predictor subspace is useful to estimate the central subspace, when conditions required in usual suffcient dimension reduction methods fail. Recently, for multivariate regression, Ko and Yoo (2022) newly defined a projective-resampling informative predictor subspace, instead of the informative predictor subspace, by the adopting projective-resampling method (Li et al. 2008). The new space is contained in the informative predictor subspace but contains the central subspace. In this paper, a method directly to estimate the informative predictor subspace is proposed, and it is compapred with the method by Ko and Yoo (2022) through theoretical aspects and numerical studies. The numerical studies confirm that the Ko-Yoo method is better in the estimation of the central subspace than the proposed method and is more efficient in sense that the former has less variation in the estimation.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.1057-1068
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• 2003

Results of using principal component analysis prior to cluster analysis are compared with results from applying agglomerative clustering algorithm alone. The retrieval ability of the agglomerative clustering algorithm is improved by using principal components prior to cluster analysis in some situations. On the other hand, the loss in retrieval ability for the agglomerative clustering algorithms decreases, as the number of informative variables increases, where the informative variables are the variables that have distinct information(or, necessary information) compared to other variables.

• Journal of the Korean Mathematical Society
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• v.35 no.3
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• pp.755-764
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• 1998

The least informative distributions minimizing Fisher information for location are obtained in the classes of continuously differentiable and piece-wise continuously differentiable densities with the additional restrictions on their values at the median and mode of population in the point and interval forms. The structure of these optimal solutions depends both on the assumptions of smoothness and form of characterizing restrictions of the class of distributions: in the class of continuously differentiable densities, the least informative distributions are finite and have the cosine-type form, and, in the class of piece-wise continuously differentiable densities, the least informative densities have exponential-type tails, the Laplace density in particular. The dependence of optimal solutions on the assumptions of symmetry is also analyzed.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.349-363
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• 2020

Penalized spline is a suitable nonparametric approach in estimating mean model in small area. However, application of the approach in informative sampling in a published article is uncommon. We propose a semiparametric mixed-model using penalized spline under informative sampling to estimate mean of small area. The response variable is explained in terms of mean model, informative sample effect, area random effect and unit error. We approach the mean model by penalized spline and utilize a penalized spline function of the inclusion probability to account for the informative sample effect. We determine the best and unbiased estimators for coefficient model and derive the restricted maximum likelihood estimators for the variance components. A simulation study shows a decrease in the average absolute bias produced by the proposed model. A decrease in the root mean square error also occurred except in some quadratic cases. The use of linear and quadratic penalized spline to approach the function of the inclusion probability provides no significant difference distribution of root mean square error, except for few smaller samples.

• Journal of Digital Contents Society
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• v.8 no.2
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• pp.157-163
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• 2007

Informative contents Industry shows stable capital structure, excellent profit index high growth rate and high productivity on the average in step with the evolution of digital technology, but it shows the aggravating trend gradually in almost all the indexes. It is because the enlargement of supply market proceeds so faster than the increase of market demand that the competition is intensified and relatively easy to advance into the market according to the development of informative contents industry. The exploitation of new market and the reclamation of new technology are needed to contrive the continuous prosperity of informative contents industry, and to achieve this, it is needed to support from politic financing and taxation politic aspect.

• Journal of Applied Reliability
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• v.6 no.3
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• pp.225-237
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• 2006

This paper proposes a method of modelling the informative dropouts with QoL(quality of life) in survival analysis. QoL is the index to measure the health related quality of life of a patient who got some treatments for a disease. Dropouts are prevalent occurrences on longitudinal study They are commonly dependent to the QoL of patients, that is, severe disease or death and called informative dropouts. Modelling the mechanism of dropouts could achieve the more accurate inference for survival analysis. A likelihood method is proposed to estimate the survival parameter and test the patterns of dropouts.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1053-1065
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• 2006

This paper proposes a method in one-way layouts for longitudinal data with informative drop-out. When dropouts are informative, that is, correlated with unobserved data and/or the previous observed data, the simple imputation methods such as 'last observation carried forward' (LOCF) methods would arise the bias of the testing models. The maximum likelihood procedure combined with a logit model for the drop-out process is proposed to test treatment effects for one factorial designs and compared with LOCF method in two examples.