• 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.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.823-829
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• 2001

Even though a rapid development of modem medical science, paralysis disease is a highly dangerous and murderous disease. Shin et al. (1978) constructed the diagnosis expert system which identify a type of the paralysis disease from symptoms of a paralysis disease patients by using the canonical discriminant analysis. The decision tree-based analysis, however, has advantages over the method used in Shin et al. (1998), such as it does not need assumptions - linearity and normality, and suggest appropriate diagnosis procedure which is easily explained. In this paper, we applied the decision tree to construct the model which Identify a type of the paralysis disease.

• Journal of Plant Biotechnology
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• v.3 no.2
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• pp.45-57
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• 2001

Trees have the ability to undergo secondary growth and produce a woody body. This tree-specific growth is affected by the secondary vascular system and the developmental continuum of secondary phloem and xylem. Secondary growth is one of the most important biological processes on earth. Considering its economic and environmental significance, our knowledge of tree growth and development is surprisingly limited. Trees have received little attention as model species in plant science, as most Plant biology questions can be best addressed by using herbaceous model species, such as Arabidopsis. Furthermore, tree biology is difficult to study mainly due to the inherent problems of tree species, including large size, long generation time, large genome size, and recalcitrance to biotechnological manipulations. Despite all of this, one must rely on trees as models to study tree-specific questions, such as secondary growth, which cannot be studied effectively in non-woody model species. Recent advances in genomics technology provide a unique opportunity to overcome these inherent tree-related problems. Several groups, including our own, have been successful in studying the biology of wood formation with a variety of hardwood and softwood species. In this article, 1 first review the current understanding of tree growth and then discuss the recent attempts to fully explore and realize the potential of molecular biology as a tool for enhanced understanding of secondary growth.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.19-27
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• 2006

This paper presents a query processing model in a wrapper based on the XML view tree. The query processing in a wrapper requires view composition, query translation into local sources, and generation of XML documents from local query results. We present a query processing model based on the view tree, where the XML views and the XML query is represented by the view tree. Since the view tree keeps the structure of a virtual XML document, it is easy to navigate the path expression. The view tree is also used as a template for schema generation and XML document generation as a query result. Moreover this conceptual uniform abstraction for the XML view and the user query makes it easy to support a multi-level XML view and to implement our composition mechanism.

• Journal of Digital Convergence
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• v.13 no.1
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• pp.243-248
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• 2015

Recognition model is not defined when you configure a model, Been added to the model after model building awareness, Model a model of the clustering due to lack of recognition models are generated by modeling is causes the degradation of the recognition rate. In order to improve decision tree state tying modeling using parameter estimation of Bayesian method. The parameter estimation method is proposed Bayesian method to navigate through the model from the results of the decision tree based on the tying state according to the maximum probability method to determine the recognition model. According to our experiments on the simulation data generated by adding noise to clean speech, the proposed clustering method error rate reduction of 1.29% compared with baseline model, which is slightly better performance than the existing approach.

• Journal of the Korean Society for Railway
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• v.12 no.6
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• pp.936-943
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• 2009

In this study, a risk assessment model based on the ETA (Event Tree Analysis) and FTA (Fault Tree Analysis) is developed according to the procedure of hazard analysis and risk assessment in order to estimate the risk quantitatively. The FTA technique is applied to estimate the branch probability (frequency) and the ETA technique is applied to estimate the consequence for each branch path on the ET (Event Tree). A risk assessment model is developed by the combination of those ETA and FTA. In addition, the reliability and the validity of the risk assessment model are verified by comparing the risk estimated through the developed model with the actual equivalent fatality.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.150-154
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• 2024

In recent years, there are extensive researches on the applications of machine learning to the automation and decision support for medical experts during disease detection. However, the performance of machine learning still needs improvement so that machine learning model produces result that is more accurate and reliable for disease detection. Selecting the hyper-parameter that could produce the possible maximum classification accuracy on medical dataset is the most challenging task in developing decision support systems with machine learning algorithms for medical dataset classification. Moreover, selecting the features that best characterizes a disease is another challenge in developing machine-learning model with better classification accuracy. In this study, we have proposed an optimized decision tree model for heart disease classification by using heart disease dataset collected from kaggle data repository. The proposed model is evaluated and experimental test reveals that the performance of decision tree improves when an optimal number of features are used for training. Overall, the accuracy of the proposed decision tree model is 98.2% for heart disease classification.

• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1137-1149
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• 2014

Since the bifurcating autoregressive (BAR) model was developed by Cowan and Staudte (1986) to analyze cell lineage data, a lot of research has been directed to BAR and its generalizations. Based mainly on the author's works, this paper is concerned with a contemporary review on the BAR in terms of an overview and perspectives. Specifically, bifurcating structure is extended to multi-cast tree and to branching tree structure. The AR(1) time series model of Cowan and Staudte (1986) is generalized to tree structured random processes. Branching correlations between individuals sharing the same parent are introduced and discussed. Various methods for estimating parameters and related asymptotics are also reviewed. Consequently, the paper aims to give a contemporary overview on the BAR model, providing some perspectives to the future works in this area.

• Journal of Korean Academy of Nursing
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• v.36 no.4
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• pp.662-670
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• 2006

Purpose. This study was performed to develop and test a decision-tree model of treatment-seeking behaviors about when Korean patients visit a doctor after experiencing stroke symptoms. Methods. The study used methodological triangulation. The model was developed based on qualitative data collected from in-depth interviews with 18 stroke patients. The model was tested using quantitative data collected from interviews and a structured questionnaire involving 150 stroke patients. The predictability of the decision-tree model was quantified as the proportion of participants who followed the pathway predicted by the model. Results. Decision outcomes of the model were categorized into immediate and delayed treatment-seeking behavior. The model was influenced by lowered consciousness, social-group influences, perceived seriousness of symptoms, past history of hypertension or stroke, and barriers to hospital visits. The predictability of the model was found to be 90.7%. Conclusions. The results from this study can help healthcare personnel understand the education needs of stroke patients regarding treatment-seeking behaviors, and hence aid in the development of educational strategies for stroke patients.

• The KIPS Transactions:PartD
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• v.11D no.7 s.96
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• pp.1367-1374
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• 2004

For sharing XML data by many users, a way of concurrency and access control is required for isolating update actions such as inserting and deleting subtrees. Exisiting locking mechanisms as 2PL or MGL suffer low concurrency when applied to tree structures. In this paper, list data subtrees model is proposed based on the semantics expressed in DTD. In this model, tree updating actions such as inserting and deleting subtrees are considered only for the repetitive parts of XML trees. The proposed model guarantees that the result XML tree after applying a tree updating action is always valid, even when multiple users access the tree at the same time. Also, a new locking mechanism called list lock-ing protocol is proposed. The new locking protocol is expected to show better accessility with less number of locking objects compared to the Helmer's OO2PL model. Since update actions on a shared XML tree usually applied to the repetitive parts of the tree, the proposed model is expected to provide a useful way for efficient data sharing when combined with previous locking methods on terminal node data.