• Korean Journal of Cognitive Science
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• v.21 no.1
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• pp.47-75
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• 2010

Two experiments were conducted to explore whether the Or structure works as a default causal model in inferring the causal structure from the contingency data. The contingencies of three unfamiliar variables were used in Experiment 1. Participants inferred the Or structure quite well from the OR data, but incorrectly inferred the Or structure from the And data for about a little less than half of the time, and almost always inferred the Or structure from the chain data. The results suggested that the Or interpretation can be the default causal model. The prevalence of the Or interpretation from the contingency data was reported even when the three variables were familiar ones in Experiment 2. Multinomial modeling performed on the results of the two experiments strongly suggested that the Or interpretation work as a default causal model.

• Journal of Environmental Science International
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• v.25 no.4
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• pp.603-613
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• 2016

Fog may have a significant impact on road conditions. In an attempt to improve fog predictability in Jeju, we conducted machine learning with various data mining techniques such as tree models, conditional inference tree, random forest, multinomial logistic regression, neural network and support vector machine. To validate machine learning models, the results from the simulation was compared with the fog data observed over Jeju(184 ASOS site) and Gosan(185 ASOS site). Predictive rates proposed by six data mining methods are all above 92% at two regions. Additionally, we validated the performance of machine learning models with WRF (weather research and forecasting) model meteorological outputs. We found that it is still not good enough for operational fog forecast. According to the model assesment by metrics from confusion matrix, it can be seen that the fog prediction using neural network is the most effective method.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1257-1270
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• 2016

This study deals with the prediction of the total number of movie audiences as a measure for the box office. Prediction is performed by classification techniques of data mining such as decision tree, multilayer perceptron(MLP) neural network model, multinomial logit model, and support vector machine over time such as before movie release, release day, after release one week, and after release two weeks. Predictors used are: online word-of-mouth(OWOM) variables such as the portal movie rating, the number of the portal movie rater, and blog; in addition, other variables include showing the inherent properties of the film (such as nationality, grade, release month, release season, directors, actors, distributors, the number of audiences, and screens). When using 10-fold cross validation technique, the accuracy of the neural network model showed more than 90 % higher predictability before movie release. In addition, it can be seen that the accuracy of the prediction increases by adding estimates of the final OWOM variables as predictors.

• Journal of Information Science Theory and Practice
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• v.2 no.3
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• pp.29-39
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• 2014

In recent years, several studies have proposed making use of the Twitter micro-blogging service to track various trends in online media and discussion. In this study, we specifically examine the use of Twitter to track discussions of food safety in the Korean language. Given the irregularity of keyword use in most tweets, we focus on optimistic machine-learning and feature set selection to classify collected tweets. We build the classifier model using Naive Bayes & Naive Bayes Multinomial, Support Vector Machine, and Decision Tree Algorithms, all of which show good performance. To select an optimum feature set, we construct a basic feature set as a standard for performance comparison, so that further test feature sets can be evaluated. Experiments show that precision and F-measure performance are best when using a Naive Bayes Multinomial classifier model with a test feature set defined by extracting Substantive, Predicate, Modifier, and Interjection parts of speech.

• Informatization Policy
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• v.26 no.1
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• pp.62-82
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• 2019

This study attempts to make an empirical analysis on how mobile devices affect users in building their social relationship and if their influences are negative or positive. The purpose of this research is to explain the results by considering all the possibilities and exploring everyday lives of using mobile devices. We used the survey data from the "Research on Mobile Environment Awareness" conducted by Gyeonggi Research Institute(GRI). The main question was about the use of mobile devices and social network services (SNS) and users' opinions on using the devices. All of the 31 municipalities in Gyeonggi Province were included as a spatial range, and the final validity sample was 1,004 residents. The extent of the relationship with people is selected as a dependent variable through the multinomial logistic model and the decision tree model. As a result of the multinomial logistic analysis on the questionnaire, the characteristics of the respondents with some changes in the scope of the human relationship were found to have a significant (+) effect on conversation with family, SNS usage, residence in the rural area but not urban area, and device usage for obtaining news. The largest variable affecting the extent of relationship was the SNS usage. As the amount of SNS usage increases, the extent of the relationship also changes a lot.

• Smart Media Journal
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• v.5 no.4
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• pp.75-82
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• 2016

Although Korea's economic and domestic automobile market through the change of road environment are growth, the traffic accident rate has also increased, and the casualties is at a serious level. For this reason, the government is establishing and promoting policies to open traffic accident data and solve problems. In this paper, describe the method of predicting traffic accidents by eliminating the class imbalance using the traffic accident data and constructing the Hybrid Model. Using the original traffic accident data and the sampled data as learning data which use FP-Growth algorithm it learn patterns associated with traffic accident injury severity. Accordingly, In this paper purpose a method for predicting the severity of a victim of a traffic accident by analyzing the association patterns of two learning data, we can extract the same related patterns, when a decision tree and multinomial logistic regression analysis are performed, a hybrid model is constructed by assigning weights to related attributes.