• 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.

• The Journal of the Korea Contents Association
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• v.20 no.10
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• pp.702-714
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• 2020

Stock investment is a personal investment technique that has gathered tremendous interest since the reduction in interest rates and tax exemption. However, it is risky especially for those who do not have expert knowledge on stock volatility. Therefore, it is well understood that accurate stock trend prediction can greatly help stock investment, giving birth to a volume of research work in the field. In order to compare different research works and to optimize hyper-parameters for prediction models, it is required to have an evaluation standard that can accurately assess performances of prediction models. However, little research has been done in the area, and conventionally used methods have been employed repeatedly without being rigorously validated. For this reason, we first analyze performance evaluation of stock trend prediction with respect to performance metrics and data composition, and propose a fair evaluation method based on prediction disparity ratio.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.2A
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• pp.98-106
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• 2005

We propose a new wireless fair queueing algorithm, WFQ-R (Wireless Fair Queueing with Retransmission), which is well matched with the LLR (Link Level Retransmission) algorithm and does not require channel prediction. In the WFQ-R algorithm, the share consumed by retransmission is regarded as a debt of the retransmitted flow to the other flows. Thus, the WFQ-R algorithm achieves wireless fairness with the LLR algorithm by penalizing flows that use wireless resources without permission under the MAC layer. Through simulations, we showed that our WFQ-R algorithm maintains fairness adaptively and maximizes system throughput. Furthermore, our WFQ-R algorithm is able to achieve flow separation and compensation.

• Journal of Information Technology Services
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• v.20 no.1
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• pp.131-143
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• 2021

Commercial transactions, one of the pillars of the capitalist economy, are occurring countless times every day, especially small and medium-sized businesses. However, small and medium-sized enterprises are bound to be the legal underdogs in contracts for commercial transactions and do not receive legal support for contracts for fair and legitimate commercial transactions. When subcontracting contracts are concluded among small and medium-sized enterprises, 58.2% of them do not apply standard contracts and sign contracts that have not undergone legal review. In order to support small and medium-sized enterprises' fair and legitimate contracts, small and medium-sized enterprises can be protected from legal threats if they can reduce the risk of signing contracts by analyzing various risks in the contract and analyzing and informing them of toxic clauses and omitted contracts in advance. We propose a risk prediction model for the machine reading-based legal contract to minimize legal damage to small and medium-sized business owners in the legal blind spots. We have established our own set of legal questions and answers based on the legal data disclosed for the purpose of building a model specialized in legal contracts. Quantitative verification was carried out through indicators such as EM and F1 Score by applying pine tuning and hostile learning to pre-learned machine reading models. The highest F1 score was 87.93, with an EM value of 72.41.

• Proceedings of the IEEK Conference
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• 2002.06a
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• pp.447-450
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• 2002

In this paper, we present design methods for a series-fed microstrip patch array operating at X-band frequency. The array consists of 18 rectangular patches connected to 3 quarter-wave impedance transformers. The power divider is designed for the uniform element excitation. The element excitation is then made to be tapered by increasing the input impedance of elements located at array edges. The designed antenna is fabricated and tested. Results of test show a fair agreement with the prediction.

• Korean Journal of Construction Engineering and Management
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• v.13 no.2
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• pp.91-101
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• 2012

In planning phase of Public Multi-Housing Projects, it is required to develop the methodology and criteria for fair cost prediction with influencing power from planning phase to occupancy phase. Many studies still have focused on the prediction of cost by multiple regression. However, there is no logical explanation about the influence of nonmetric variables for the prediction of cost in planning phase. Accordingly, this research pursues a cost prediction model including nonmetric variables for use in planning phase. There are 3 steps of this research : 1) Finding the factors influencing construction cost and assigning variables for a multiple regression. 2) Conducting a dummy regression analysis with nonmetric variables and model validation by comparing actual cost data. 3) Developing the ratio of RC structure cost to wall structure cost by using cost predection model. The results could establish cost prediction process including the influence of nonmetric variables and the ratio of RC structure cost to wall structure cost.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1088-1090
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• 1999

The radio noise produced by corona discharge in high voltage transmission tines is one of the most important line design considerations. Therefore it is necessary to pre-evaluate radio noise for transmission line designers using Prediction formulas or field test results. In this Paper, more accurate and useful formulas for Predicting radio noise during fair and foul weathers in AC transmission lines were proposed through comparison with the existing formulas. Also it was verified by comparing with the long-term measured data from operating lines that the Proposed formulas are very accurate. The Proposed prediction formulas are developed by the applications of nonlinear least square optimization method to radio noise database collected from lines throughout the world.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.795-804
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• 2006

Most internet auction sites open to users the bid history with the ascending order of bid amounts. Whereas eBay.com presents second bid prices, auction.co.kr provides highest bid prices. In this paper, the bidhistory is arranged according to the passage of tim, which can help to understand the situations and trends of bid prices, especially for multiple auctions. This manipulated data can be visualized by using profile plots. The successful bid prices could be estimated based on some prediction models with appropriate prior informations. Both sellers and bidders can be provided useful informations with these statistical analyses, and then fair online auctions in Korea will grow actively and rapidly.

• Journal of Biosystems Engineering
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• v.25 no.3
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• pp.195-202
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• 2000

This study was conducted to recognize a possibility that cone index can be used as a means of evaluating the tillage workability. Cone indexes were measured every 24 hours after rainfall at the experimental plots, and the rotary and plowing operations were conducted at the same time. The workability was evaluated on a basis of three categories of good, fair and poor depending on the quality of the performed works. Although the workability was affected by many factors such as soil type, moisture content ground slope and weather condition, the duration and amount of rainfall were of most influence. Results of the study showed that a good workability was resulted from the cone indexes greater than an average of 552 kPa for rotary operations and 671 kPa for plowing operations. Fair work was obtained with cone indexes greater than an average of 331 kPa for rotary operations and 459 kPa for plowing operations. The cone indexes less than an average of 171 kPa and 149 kPa resulted in poor workabilities for rotary and plowing operations, respectively. The experimental results may provide a general guideline for evaluating the tillage workability by cone index.

• The Korean Journal of Applied Statistics
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• v.31 no.3
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• pp.367-382
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• 2018

Prediction models for a corporate bond rating in existing studies have been developed using various models such as linear regression, ordered logit, and random forest. Financial characteristics help build prediction models that are expected to be contained in the assigning model of the bond rating agencies. However, the ranges of bond ratings in existing studies vary from 5 to 20 and the prediction models were developed with samples in which the target companies and the observation periods are different. Thus, a simple comparison of the prediction accuracies in each study cannot determine the best prediction model. In order to conduct a fair comparison, this study has collected corporate bond ratings and financial characteristics from 2013 to 2017 and applied prediction models to them. In addition, we applied the elastic-net penalty for the linear regression, the ordered logit, and the ordered probit. Our comparison shows that data-driven variable selection using the elastic-net improves prediction accuracy in each corresponding model, and that the random forest is the most appropriate model in terms of prediction accuracy, which obtains 69.6% accuracy of the exact rating prediction on average from the 5-fold cross validation.