• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.386-390
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• 2003

Data mining is a process of discovering useful patterns for decision making from an amount of data. It has recently received much attention in a wide range of business and engineering fields. Classifying a group into subgroups is one of the most important subjects in data mining. Tree-based methods, known as decision trees, provide an efficient way to finding the classification model. The primary concern in tree learning is to minimize a node impurity, which is evaluated using a target variable in the data set. However, there are situations where multiple target variable should be taken into account, for example, such as manufacturing process monitoring, marketing science, and clinical and health analysis. The purpose of this article is to present some methods for measuring the node impurity, which are applicable to data sets with multivariate target variables. For illustration, a numerical cxample is given with discussion.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.45-53
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• 2003

One of decision tree method is regression trees which are used to predict a continuous response. The general splitting criteria in tree growing are based on a compromise in the impurity between the left and the right child node. By picking or the more interesting subsets and ignoring the other, the proposed new splitting criteria in this paper do not split based on a compromise of child nodes anymore. The tree structure by the new criteria might be unbalanced but plausible. It can find a interesting subset as early as possible and express it by a simple clause. As a result, it is very interpretable by sacrificing a little bit of accuracy.

• KIISE Transactions on Computing Practices
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• v.22 no.12
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• pp.681-686
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• 2016

Software defect prediction is an important factor in efficient project management and success. The severity of the defect usually determines the degree to which the project is affected. However, existing studies focus only on the presence or absence of a defect and not the severity of defect. In this study, we proposed an ensemble model using FCM based on defect severity. The severity of the defect of NASA data set's PC4 was reclassified. To select the input column that affected the severity of the defect, we extracted the important defect factor of the data set using Random Forest (RF). We evaluated the performance of the model by changing the parameters in the 10-fold cross-validation. The evaluation results were as follows. First, defect severities were reclassified from 58, 40, 80 to 30, 20, 128. Second, BRANCH_COUNT was an important input column for the degree of severity in terms of accuracy and node impurities. Third, smaller tree number led to more variables for good performance.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.5
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• pp.69-76
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• 2016

Current driving capability of MCT (MOS Controlled Thyristor) is determined by turn-off capability of conducting current, that is off-FET performance of MCT. On the other hand, having a good turn-on characteristics, including high peak anode current ($I_{peak}$) and rate of change of current (di/dt), is essential for pulsed power system which is one of major application field of MCTs. To satisfy above two requirements, careful control of on/off-FET performance is required. However, triple diffusion and several oxidation processes change surface doping profile and make it hard to control threshold voltage ($V_{th}$) of on/off-FET. In this paper, we have demonstrated the effect of $V_{th}$ adjustment ion implantation on the performance of MCT. The fabricated MCTs (active area = $0.465mm^2$) show forward voltage drop ($V_F$) of 1.25 V at $100A/cm^2$ and Ipeak of 290 A and di/dt of $5.8kA/{\mu}s$ at $V_A=800V$. While these characteristics are unaltered by $V_{th}$ adjustment ion implantation, the turn-off gate voltage is reduced from -3.5 V to -1.6 V for conducting current of $100A/cm^2$ when the $V_{th}$ adjustment ion implantation is carried out. This demonstrates that the current driving capability is enhanced without degradation of forward conduction and turn-on switching characteristics.