• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.553-562
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• 2004

In this short communication we discuss the bias problems of CART in split variable selection and suggest a method to reduce the variable selection bias. Penalties proportional to the number of categories or distinct values are applied to the splitting criteria of CART. The results of empirical comparisons show that the proposed modification of CART reduces the bias in variable selection.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.251-261
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• 2022

As AI has a wide range of influence on human social life, issues of transparency and ethics of AI are emerging. In particular, it is widely known that due to the existence of historical bias in data against ethics or regulatory frameworks for fairness, trained AI models based on such biased data could also impose bias or unfairness against a certain sensitive group (e.g., non-white, women). Demographic disparities due to AI, which refer to socially unacceptable bias that an AI model favors certain groups (e.g., white, men) over other groups (e.g., black, women), have been observed frequently in many applications of AI and many studies have been done recently to develop AI algorithms which remove or alleviate such demographic disparities in trained AI models. In this paper, we consider a problem of using the information in the sensitive variable for fair prediction when using the sensitive variable as a part of input variables is prohibitive by laws or regulations to avoid unfairness. As a way of reflecting the information in the sensitive variable to prediction, we consider a two-stage procedure. First, the sensitive variable is fully included in the learning phase to have a prediction model depending on the sensitive variable, and then an imputed sensitive variable is used in the prediction phase. The aim of this paper is to evaluate this procedure by analyzing several benchmark datasets. We illustrate that using an imputed sensitive variable is helpful to improve prediction accuracies without hampering the degree of fairness much.

• 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.

• Journal of Advanced Navigation Technology
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• v.13 no.3
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• pp.358-364
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• 2009

This paper shows some variable bias techniques which can improve the power added efficiency(PAE) for the designed power amplifier. Some simulations have been done to get the effect of the bias change, and variable bias is adopted to get the higher efficiency for dual mode amplifier which generates two different output power levels. With drain bias change and a fixed gate bias, the amplifier shows PAE improvement compared to the fixed bias amplifier. In addition, this paper analyzed nonlinear distortion of the power amplifier and has used the digital predistortion which can result in 10dB ACPR improvement for the dual band amplifier.

• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.459-473
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• 2004

It has well known that an exhaustive search algorithm suggested by Breiman et. a1.(1984) has a trend to select the variable having relatively many possible splits as an splitting rule. We propose an algorithm to overcome this variable selection bias problem and then construct unbiased regression trees based on the algorithm. The proposed algorithm runs two steps of selecting a split variable and determining a split rule for binary split based on the split variable. Simulation studies were performed to compare the proposed algorithm with Breiman et a1.(1984)'s CART(Classification and Regression Tree) in terms of degree of variable selection bias, variable selection power, and MSE(Mean Squared Error). Also, we illustrate the proposed algorithm with real data sets.

• Journal of Korean Academy of Nursing
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• v.38 no.3
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• pp.403-409
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• 2008

Purpose: This study was performed to identify the relationship between optimistic bias about health crisis and health behavior of Korean adults in a crisis of health, and to prepare baseline data for developing a health education and promotion program. Methods: Study subjects were 595 aged from 19 to 64 who live in Korea. Data were collected through questionnaires administered by one interviewer. Descriptive statistics and Pearson's correlation coefficient were calculated using the SPSS program. Results: The average score for optimistic bias about health crisis was 2.69, and that for health behavior was 107.05. The optimistic bias about health crisis showed a significantly positive correlation with health behavior (r=.187, p=.000). Conclusion: To make our results more useful, it is necessary to identity the causal relationship between health attitudes as an explanatory variable and optimistic bias as an outcome variable. In addition, a relatively low score in optimistic bias from this research compared to other studies must be explained through further studies considering unique Korean cultural background. Moreover, research of the relationship between optimistic bias about health crisis and health behavior looking at people who don't have good health behaviors is needed.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.161-161
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• 2023

Bias correction of values is a necessary step in downscaling coarse and systematically biased global climate models for use in local climate change impact studies. In addition to univariate bias correction methods, many multivariate methods which correct multiple variables jointly - each with their own mathematical designs - have been developed recently. While some literature have focused on the inter-comparison of these multivariate bias correction methods, none have focused extensively on the effect of diverse configurations (i.e., different combinations of input variables to be corrected) of climate variables, particularly high-dimensional ones, on the ability of the different methods to remove biases in uni- and multivariate statistics. This study evaluates the impact of three configurations (inter-variable, inter-spatial, and full dimensional dependence configurations) on four state-of-the-art multivariate bias correction methods in a national-scale domain over South Korea using a gridded approach. An inter-comparison framework evaluating the performance of the different combinations of configurations and bias correction methods in adjusting various climate variable statistics was created. Precipitation, maximum, and minimum temperatures were corrected across 306 high-resolution (0.2°) grid cells and were evaluated. Results show improvements in most methods in correcting various statistics when implementing high-dimensional configurations. However, some instabilities were observed, likely tied to the mathematical designs of the methods, informing that some multivariate bias correction methods are incompatible with high-dimensional configurations highlighting the potential for further improvements in the field, as well as the importance of proper selection of the correction method specific to the needs of the user.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.809-824
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• 2002

we propose two methods which separate the variable selection step and the split-point selection step. We call these two algorithms as CHITES method and F&CHITES method. They adapted some of the best characteristics of CART, CHAID, and QUEST. In the first step the variable, which is most significant to predict the target class values, is selected. In the second step, the exhaustive search method is applied to find the splitting point based on the selected variable in the first step. We compared the proposed methods, CART, and QUEST in terms of variable selection bias and power, error rates, and training times. The proposed methods are not only unbiased in the null case, but also powerful for selecting correct variables in non-null cases.

• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.347-357
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• 2004

It is very important to select a split variable in constructing the classification tree. The efficiency of a classification tree algorithm can be evaluated by the variable selection bias and the variable selection power. The C4.5 has largely biased variable selection due to the influence of many distinct values in variable selection and the QUEST has low variable selection power when a continuous predictor variable doesn't deviate from normal distribution. In this thesis, we propose the SRT algorithm which overcomes the drawback of the C4.5 and the QUEST. Simulations were performed to compare the SRT with the C4.5 and the QUEST. As a result, the SRT is characterized with low biased variable selection and robust variable selection power.

• Progress in Superconductivity and Cryogenics
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• v.8 no.2
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• pp.17-19
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• 2006

A novel clock distribution technique for pipelined-RSFQ logics using variable Bias Currents of JTLs as delay-medium is newly proposed. RSFQ logics consist of several logic gates or blocks connected in a pipeline structure. And each block has variable delay difference. In the structure, this clock distribution method generates a set of clock signals for each logic blocks with suitable corresponding delays. These delays, in the order of few to tens of pS, can be adjusted through controlling bias current of JTL of delay medium. While delays with resistor value and JJ size are fixed at fabrication stage, delay through bias current can be controlled externally, and thus, is heavily investigated for its range as well as correct operation within current margin. Possible ways of a standard delay library with modular structure are sought for further modularizing Pipelined-RSFQ applications. Simulations and verifications are done through WRSpice with Hypres 3-um process parameters.