• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.89-94
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• 2001

In this study we focus on variable selection in decision tree growing structure. Some of the splitting rules and variable selection algorithms are discussed. We propose a competitive variable selection method based on Kruskal-Wallis test, which is a nonparametric version of ANOVA F-test. Through a Monte Carlo study we note that CART has serious bias in variable selection towards categorical variables having many values, and also QUEST using F-test is not so powerful to select informative variables under heavy tailed distributions.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.239-247
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• 2003

Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud dection, data reduction and variable screening, etc. CHAID(Chi-square Automatic Interaction Detector), is an exploratory method used to study the relationship between a dependent variable and a series of predictor variables. In this paper we propose and CHAID algorithm by cube-based sampling and explore CHAID algorithm in view of accuracy and speed by the number of variables.

• Journal of the Korea Safety Management & Science
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• v.15 no.2
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• pp.243-253
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• 2013

This research paper introduces the application and implementation of medical decision metrics that classifies medical decision-making into four different metrics using statistical diagnostic tools, such as confusion matrix, normal distribution, Bayesian prediction and Receiver Operating Curve(ROC). In this study, the metrics are developed based on cross-section study, cohort study and case-control study done by systematic literature review and reformulated the structure of type I error, type II error, confidence level and power of detection. The study proposed implementation strategies for 10 quality improvement activities via 14 medical decision metrics which consider specificity and sensitivity in terms of ${\alpha}$ and ${\beta}$. Examples of ROC implication are depicted in this paper with a useful guidelines to implement a continuous quality improvement, not only in a variable acceptance sampling in Quality Control(QC) but also in a supplier grading score chart in Supplier Chain Management(SCM) quality. This research paper is the first to apply and implement medical decision-making tools as quality improvement activities. These proposed models will help quality practitioners to enhance the process and product quality level.

• Proceedings of the IEEK Conference
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• 2001.06e
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• pp.207-210
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• 2001

This Paper presents a design of the fuzzy decision maker analyzable of output result of ECG signals. The fuzzy decision maker proposed are divided into two groups whose functions are different each other. The one rules when decision of heart rates, The other decision values for an interval of each points of waveform using of which static state values and abnormal values. We have chosen several variable used for composing condition and action part by knowledge of an Expert The result of outputs with fuzzy rules suggested was a proved of satisfied with by classify ECG arrythmia signals

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.149-161
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• 2006

The most important component in decision tree algorithm is the rule for split variable selection. Many earlier algorithms such as CART and C4.5 use greedy search algorithm for variable selection. Recently, many methods were developed to cope with the weakness of greedy search algorithm. Most algorithms have different selection criteria depending on the type of variables: continuous or nominal. However, ordinal type variables are usually treated as continuous ones. This approach did not cause any trouble for the methods using greedy search algorithm. However, it may cause problems for the newer algorithms because they use statistical methods valid for continuous or nominal types only. In this paper, we propose a ordinal variable selection method that uses Cramer-von Mises testing procedure. We performed comparisons among CART, C4.5, QUEST, CRUISE, and the new method. It was shown that the new method has a good variable selection power for ordinal type variables.

• The Journal of Information Technology
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• v.2 no.2
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• pp.51-67
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• 1999

This study investigates the effect of information about risk on investment decision making. The model was constructed with two independent variables and two dependant variables. The first independent variable deals with whether information about risk is provided or not. The meaning of information about risk is the degree of uncertainty for investment decision making. The other independent variable involves attitude toward risk, such as the degree of tolerance for risk. Two dependant variables are concerned with the effect on investment decision making, such as the degree of riskiness of decision making and decision confidence, respectively. This study deals with the investment decision in options on stock indices. The findings of this study are as follows. The more the degree of tolerance for risk, the more the degree of decision confidence. This means that risk-taker has more desire for achievement than risk-avoider has. And the message alerting risk of investment decision making takes little effect on investment decision making.

• Journal of Korea Multimedia Society
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• v.16 no.4
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• pp.502-509
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• 2013

In Information age, The academic liberal art computer education course set up goals to promote computer literacy and develop the ability to cope with changes in information society and improve productivity and national competitiveness. In this paper, we analyze on discovering of decisive variable and satisfaction index to have a influence on computer education on university students. As a preprocessing course, the proposed method selects optimum variable using correlation based feature selection(CFS) of machine learning tool based on Java and we calculate weighted value for each variable and then, we generate the optimal variable using weighted value based on fuzzy decision making method. we proposed Fuzzy decision making method in analysis of the academic liberal art computer education satisfaction index data and checked the accuracy of the satisfaction evaluation by using recall and precision.

• Journal of Digital Convergence
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• v.15 no.12
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• pp.169-185
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• 2017

Given that nowadays things get very fast due to the pervasive use of the Internet and mobile devices, decision-making time can be an important variable in the online economic decisions. Although in experimental and behavioral economics, measures like scores or earnings are usually preferred, this study argues that the time variable can be dealt with as a new decision outcome. Thus, by selecting some psychological factors presumably impactful in the online context (i.e., incidental emotions, psychological distances, and individual's impulsivity), this study tested their effect on decision time in the online centipede game. As a result, the mean decision time in the game was longer (1) in the happiness condition than in the anger condition and (2) in the friend condition than in the stranger condition. The people with attention difficulties spent a short time in the decision and the people who dislike complex problems spent a short time in explaining their decision. This study can contribute to the field as it used the decision time as the dependent variable and it tested the effect of psychological factors in the context of online decision-making. Future studies can be conducted in other online decision situations or by considering other psychological variables.

• Journal of Communications and Networks
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• v.17 no.3
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• pp.213-220
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• 2015

In this paper, we present a reliability-based iterative proportionality-logic decoding algorithm for two classes of structured low-density parity-check (LDPC) codes. The main contributions of this paper include: 1) Syndrome messages instead of extrinsic messages are processed and exchanged between variable nodes and check nodes, which can reduce the decoding complexity; 2) a more flexible decision mechanism is developed in which the decision threshold can be self-adjusted during the iterative process. Such decision mechanism is particularly effective for decoding the majority-logic decodable codes; 3) only part of the variable nodes satisfying the pre-designed criterion are involved for the presented algorithm, which is in the proportionality-logic sense and can further reduce the computational complexity. Simulation results show that, when combined with factor correction techniques and appropriate proportionality parameter, the presented algorithm performs well and can achieve fast decoding convergence rate while maintaining relative low decoding complexity, especially for small quantized levels (3-4 bits). The presented algorithm provides a candidate for those application scenarios where the memory load and the energy consumption are extremely constrained.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.6 no.8
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• pp.63-71
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• 1983

Aggregate planning coordinate the control variable over long-term to apply a demand variable and forcasting. In order to necessary the goal that doesn't make an inter-contradiction and explicitly defined. We made a considerable point of system approach for scheduling establishment. It include the control variables of aggregate planning : 1) employment 2) over time working and idle time 3) inventory 4) delivery delay S) subcontract 61 long - term facility capacity. Each variables composed of pure strategy as like a decision of inventory level, a change of employment level, etc. md alternative costs make a computation on the economic foundation. But the optimum alternative costs represent the mixed pure strategy. The faults of this method doesn't optimum guarantee a special scheduling as well as increasing a number of alternative combination. Theoretical, Linear Decision Rule make an including all variables, but it is almost impossible for this model to develope actually And also make use of the aggregate planning problem for developing system approach : LDR, heuristic model, Search Decision Rule, all kind of computers, simulation. But these models are very complex, each variables get an extremely inter-dependence. So this study be remained by theory level, some approach methods has not been brought the optimum solution to apply in every cases.