• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.57-65
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• 1998

This paper addresses the data classification using the induction of decision trees. A weakness of other techniques of induction of decision trees is that decision trees are too large because they construct decision trees until leaf nodes have a single class. Our study include both overcoming this weakness and constructing decision trees which is small and accurate. First, we construct the decision trees using classification threshold and exception threshold in construction stage. Next, we present two stage pruning method using classification threshold and reduced error pruning in pruning stage. Empirical results show that our method obtain the decision trees which is accurate and small.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.885-894
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• 2001

Decision tree methods is the one of data mining techniques. Classification trees are used to predict a class label. When a tree grows, the conventional splitting criteria use the weighted average of the left and the right child nodes for measuring the node impurity. In this paper, new splitting criteria for classification trees are proposed which improve the interpretablity of trees comparing to the conventional methods. The criteria search only for interesting subsets of the data, as opposed to modeling all of the data equally well. As a result, the tree is very unbalanced but extremely interpretable.

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.79-89
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• 2016

In this paper, we proposed the object classification method using genetic and dynamic random forest consisting of optimal combination of unit tree. The random forest can ensure good generalization performance in combination of large amount of trees by assigning the randomization to the training samples and feature selection, etc. allocated to the decision tree as an ensemble classification model which combines with the unit decision tree based on the bagging. However, the random forest is composed of unit trees randomly, so it can show the excellent classification performance only when the sufficient amounts of trees are combined. There is no quantitative measurement method for the number of trees, and there is no choice but to repeat random tree structure continuously. The proposed algorithm is composed of random forest with a combination of optimal tree while maintaining the generalization performance of random forest. To achieve this, the problem of improving the classification performance was assigned to the optimization problem which found the optimal tree combination. For this end, the genetic algorithm methodology was applied. As a result of experiment, we had found out that the proposed algorithm could improve about 3~5% of classification performance in specific cases like common database and self infrared database compare with the existing random forest. In addition, we had shown that the optimal tree combination was decided at 55~60% level from the maximum trees.

• Journal of Korean Society for Quality Management
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• v.30 no.1
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• pp.152-161
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• 2002

The analysis of large data sets with hundreds of thousands observations and thousands of independent variables is a formidable computational task. A less parametric method, capable of identifying important independent variables and their interactions, is a tree structured approach to regression and classification. It gives a graphical and often illuminating way of looking at data in classification and regression problems. In this paper, we have reviewed and summarized tile methodology used to construct a tree, multiple trees and the sequential strategy for identifying active compounds in large chemical databases.

• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.543-559
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• 2017

Tree-based regression and classification ensembles form a standard part of the data-science toolkit. Many commonly used methods take an algorithmic view, proposing greedy methods for constructing decision trees; examples include the classification and regression trees algorithm, boosted decision trees, and random forests. Recent history has seen a surge of interest in Bayesian techniques for constructing decision tree ensembles, with these methods frequently outperforming their algorithmic counterparts. The goal of this article is to survey the landscape surrounding Bayesian decision tree methods, and to discuss recent modeling and computational developments. We provide connections between Bayesian tree-based methods and existing machine learning techniques, and outline several recent theoretical developments establishing frequentist consistency and rates of convergence for the posterior distribution. The methodology we present is applicable for a wide variety of statistical tasks including regression, classification, modeling of count data, and many others. We illustrate the methodology on both simulated and real datasets.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.274-283
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• 2002

This paper presents an efficient models for reinforeced concrete structures using CART-ANFIS(classification and regression tree-adaptive neuro fuzzy inference system). a fuzzy decision tree parttitions the input space of a data set into mutually exclusive regions, each of which is assigned a label, a value, or an action to characterize its data points. Fuzzy decision trees used for classification problems are often called fuzzy classification trees, and each terminal node contains a label that indicates the predicted class of a given feature vector. In the same vein, decision trees used for regression problems are often called fuzzy regression trees, and the terminal node labels may be constants or equations that specify the Predicted output value of a given input vector. Note that CART can select relevant inputs and do tree partitioning of the input space, while ANFIS refines the regression and makes it everywhere continuous and smooth. Thus it can be seen that CART and ANFIS are complementary and their combination constitutes a solid approach to fuzzy modeling.

• Journal of Ecology and Environment
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• v.35 no.2
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• pp.91-98
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• 2012

This study was performed in order to suggest the possibility of tree species classification using high-resolution QuickBird-2 images spectral characteristics comparison(digital numbers [DNs]) of tree species, tree species classification, and accuracy verification. In October 2010, the tree species of three conifers and eight broad-leaved trees were examined in the areas studied. The spectral characteristics of each species were observed, and the study area was classified by image classification. The results were as follows: Panchromatic and multi-spectral band 4 was found to be useful for tree species classification. DNs values of conifers were lower than broad-leaved trees. Vegetation indices such as normalized difference vegetation index (NDVI), soil brightness index (SBI), green vegetation index (GVI) and Biband showed similar patterns to band 4 and panchromatic (PAN); Tukey's multiple comparison test was significant among tree species. However, tree species within the same genus, such as $Pinus$ $densiflora-P.$ $rigida$ and $Quercus$ $mongolica-Q.$ $serrata$, showed similar DNs patterns and, therefore, supervised classification results were difficult to distinguish within the same genus; Random selection of validation pixels showed an overall classification accuracy of 74.1% and Kappa coefficient was 70.6%. The classification accuracy of $Pterocarya$ $stenoptera$, 89.5%, was found to be the highest. The classification accuracy of broad-leaved trees was lower than expected, ranging from 47.9% to 88.9%. $P.$ $densiflora-P.$ $rigida$ and $Q.$ $mongolica-Q.$ $serrata$ were classified as the same species because they did not show significant differences in terms of spectral patterns.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.113-120
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• 2024

With the advancement of information and communication technology, we can easily generate various forms of data in our daily lives. To efficiently manage such a large amount of data, systematic classification into categories is essential. For effective search and navigation, data is organized into a tree-like hierarchical structure known as a category tree, which is commonly seen in news websites and Wikipedia. As a result, various techniques have been proposed to classify large volumes of documents into the terminal nodes of category trees. However, document classification methods using category trees face a problem: as the height of the tree increases, the number of terminal nodes multiplies exponentially, which increases the probability of misclassification and ultimately leads to a reduction in classification accuracy. Therefore, in this paper, we propose a new node expansion-based classification algorithm that satisfies the classification accuracy required by the application, while enabling detailed categorization. The proposed method uses a greedy approach to prioritize the expansion of nodes with high classification accuracy, thereby maximizing the overall classification accuracy of the category tree. Experimental results on real data show that the proposed technique provides improved performance over naive methods.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.565-574
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• 2004

Classification tree algorithms including as CART by Brieman et al.(1984) in some aspects, recursively partition the data space with the aim of making the distribution of the class variable as pure as within each partition and consist of several steps. SUPPORT(smoothed and unsmoothed piecewise-polynomial regression trees) method of Chaudhuri et al(1994), a weighted averaging technique is used to combine piecewise polynomial fits into a smooth one. We focus on applying SUPPORT to a binary class variable. Logistic model is considered in the caculation techniques and the results are shown good classification rates compared with other methods as CART, QUEST, and CHAID.

• Journal of Korea Society of Digital Industry and Information Management
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• v.20 no.2
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• pp.1-10
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• 2024

For the purpose of predicting credit card customer churn accurately through data analysis, a model can be constructed with various machine learning algorithms, including decision tree. And feature importance has been utilized in selecting better input features that can improve performance of data analysis models for several application areas. In this paper, a method of utilizing feature importance calculated from the MDI method and its effects are investigated in the credit card customer churn prediction problem with classification trees. Compared with several random feature selections from case data, a set of input features selected from higher value of feature importance shows higher predictive power. It can be an efficient method for classifying and choosing input features necessary for improving prediction performance. The method organized in this paper can be an alternative to the selection of input features using feature importance in composing and using classification trees, including credit card customer churn prediction.