• Proceedings of the Korea Society of Design Studies Conference
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• 2004.05a
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• pp.24-25
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• 2004

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.299-307
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• 2012

Data mining is a method of searching for an interesting relationship among items in a given database. The decision tree is a typical algorithm of data mining. The decision tree is the method that classifies or predicts a group as some subgroups. In general, when researchers create a decision tree model, the generated model can be complicated by the standard of model creation and the number of input variables. In particular, if the decision trees have a large number of input variables in a model, the generated models can be complex and difficult to analyze model. When creating the decision tree model, if there are marginally conditional variables (intervening variables, external variables) in the input variables, it is not directly relevant. In this study, we suggest the method of creating a decision tree using marginally conditional variables and apply to actual data to search for efficiency.

• Journal of the Korean Data and Information Science Society
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• v.23 no.1
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• pp.99-111
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• 2012

The log-density ratio of the conditional densities of the predictors given the response variable provides useful information for variable selection in the logistic regression model. In this paper, we consider the predictors that are needed and how they should be included in the model. If the conditional distributions are skewed, the distributions can be considered as gamma distributions. Under this assumption, linear and log terms are generally included in the model. The log-odds graph is a very useful graphical tool in this study. A graphical study is presented which shows that if the conditional distributions of x|y for the two groups overlap significantly, we need both the linear and quadratic terms. On the contrary, if they are well separated, only the linear or log term is needed in the model.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.1
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• pp.10-16
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• 2015

In this study, we suggest a method to predict probability distribution of a new customer's degree of loyalty using C-CRF that reflects the RFM score and similarity to the neighbors of the customer. An RFM score prediction model is introduced to construct the first feature function of C-CRF. Integrating demographical similarity, purchasing characteristic similarity and purchase history similarity, we make a unified similarity variable to configure the second feature function of C-CRF. Then parameters of each feature function are estimated and we train our C-CRF model by training data set and suggest a probabilistic distribution to estimate a new customer's degree of loyalty. An example is provided to illustrate our model.

• The Transactions of the Korea Information Processing Society
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• v.1 no.1
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• pp.38-49
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• 1994

One of the most common problems encountered in the automatic translation of FORTRAN source code to VECTRAN is the occurrence of conditional transfer of control within loops. Transfers of control create control dependencies, in which the execution of a statement is dependent on the value of a variable in another statement. In this paper I propose algorithms involve an attempt to convert statements in the loop into conditional assignment statements that can be easily analyzed for data dependency, and this paper presents a simplification method for conditional assignment statement. Especially, I propose not only a method for simplifying boolean functions but extended method for n-state functions.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.847-851
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• 2004

In the original mixture density network(MDN), which was introduced by Bishop and Nabney, the parameters of the conditional probability density function are represented by the output vector of a single multi-layer perceptron. Among the recent modification of the MDNs, there is the so-called modified mixture density network, in which each of the priors, conditional means, and covariances is represented via an independent multi-layer perceptron. In this paper, we consider a further simplification of the modified MDN, in which the conditional means are linear with respect to the input variable together with the development of the MATLAB program for the simplification. In this paper, we first briefly review the original mixture density network, then we also review the modified mixture density network in which independent multi-layer perceptrons play an important role in the learning for the parameters of the conditional probability, and finally present a further modification so that the conditional means are linear in the input. The applicability of the presented method is shown via an illustrative simulation example.

• Communications for Statistical Applications and Methods
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• v.20 no.3
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• pp.169-174
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• 2013

The ROC curve is drawn with two conditional cumulative distribution functions (or survival functions) of the univariate random variable. In this work, we consider joint cumulative distribution functions of k random variables, and suggest a ROC curve for multivariate random variables. With regard to the values on the line, which passes through two mean vectors of dichotomous states, a joint cumulative distribution function can be regarded as a function of the univariate variable. After this function is modified to satisfy the properties of the cumulative distribution function, a ROC curve might be derived; moreover, some illustrative examples are demonstrated.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.3
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• pp.200-205
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• 2000

An economic process monitoring procedure is presented using a surrogate variable for the case where performance variable is dichotomous. Every item is inspected with a surrogate variable and determined whether it should be accepted or rejected. When an item is rejected, the previous number of consecutively accepted items is compared with a predetermined number r to decide whether there is a shift in fraction nonconforming or not. The conditional distribution of the surrogate variable given the performance variable is assumed to be normal. A cost model is constructed which includes costs of inspection, misclassification, illegal signal, undetected out-of-control state, and correction. Methods of finding the optimum number r and screening limit are provided. Numerical studies on the effects of cost coefficients are also performed.

• Communications for Statistical Applications and Methods
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• v.1 no.1
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• pp.33-40
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• 1994

The conditional expectation of a random variable in a multivariate normal random vector is a multiple linear regression on its predecessors. Using this fact, the least median of squares estimation method developed in a multiple linear regression is adapted to a multivariate data to identify influential observations. The resulting method clearly detect outliers and it avoids the masking effect.

• Management & Information Systems Review
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• v.24
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• pp.97-124
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• 2008

This study aimed to explore how the involvement of environment influenced eight subjects group. Thus, experiment was performed to clarify the role that the attitude of university student consumer plays in the communication process depending on the level of engagement of consumer in the environment and method to raise donation for preservation of environment. Analyzing as per the type of appeal, the mark in altruistic appeal type was higher in all variables than egoistic appeal type. Finally, checking the average mark of each variable as per the condition of donation, the value in unconditional donation was higher than in all variables than conditional donation. It was found 3 groups composed of 2 groups with high level of environmental engagement and 1 group with low level of environmental engagement were suitable to double mediation model among the 8 experimental groups. The group where double mediation model best corresponds than any other group was high level related to environment and the group that contacts altruistic appeal and the message in the form of conditional donation. It was also found that the group that has low level of environmental engagement and contacts egoistic appeal type and conditional donation shows the group that corresponds to double mediation model in the second place among the 8 groups. Finally, it was found that the group that has high level of environmental engagement and is stimulated by altruistic appeal and unconditional donation corresponds to double mediation model. Depending on the condition of message stimulation, unconditional donation is found to better correspond to double mediation model than conditional donation. However, opposite phenomena is observed when the level of environmental engagement is high and appeal type is egoistic. Namely, it was found that conditional donation better corresponds to double mediation model than unconditional donation.