• Journal of KIISE:Databases
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• v.33 no.5
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• pp.463-475
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• 2006

In applications such as a traffic management system, analysis using a spatial hierarchy of a spatial data warehouse and a simple aggregation is required. Over the past few years, several studies have been made on solution using a spatial index. Many studies have focused on using extended R-tree. But, because it just provides either the current aggregation or the total aggregation, decision support of traffic policy required historical analysis can not be provided. This paper proposes hybrid index based on extended aR-tree for the spatio-temporal aggregation. The proposed method supports a spatial hierarchy and the current aggregation by the R-tree. The sorted hash table using the time structure of the extended aR-tree provides a temporal hierarchy and a historical aggregation. Therefore, the proposed method supports an efficient decision support with spatio-temporal analysis and is Possible currently traffic analysis and determination of a traffic policy with historical analysis.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.643-652
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• 2006

Decision trees are mainly used for the classification and prediction in data mining. The distribution of spatial data and relationships with their neighborhoods are very important when conducting classification for spatial data mining in the real world. Spatial decision trees in previous works have been designed for reflecting spatial data characteristic by rating Euclidean distance. But it only explains the distance of objects in spatial dimension so that it is hard to represent the distribution of spatial data and their relationships. This paper proposes a decision tree based on spatial entropy that represents the distribution of spatial data with the dispersion and dissimilarity. The dispersion presents the distribution of spatial objects within the belonged class. And dissimilarity indicates the distribution and its relationship with other classes. The rate of dispersion by dissimilarity presents that how related spatial distribution and classified data with non-spatial attributes we. Our experiment evaluates accuracy and building time of a decision tree as compared to previous methods. We achieve an improvement in performance by about 18%, 11%, respectively.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.4
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• pp.101-111
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• 2014

Spatial events are predictable using data mining classification algorithms. Decision trees have been used as one of representative classification algorithms. And they were normally used in the classification tasks that have label class values. However since using rule ranking methods, spatial prediction have been applied in the spatial prediction problems. This paper compared rule ranking methods for the spatial prediction application using a decision tree. For the comparison experiment, C4.5 decision tree algorithm, and rule ranking methods such as Laplace, M-estimate and m-branch were implemented. As a spatial prediction case study, landslide which is one of representative spatial event occurs in the natural environment was applied. Among the rule ranking methods, in the results of accuracy evaluation, m-branch showed the better accuracy than other methods. However in case of m-brach and M-estimate required additional time-consuming procedure for searching optimal parameter values. Thus according to the application areas, the methods can be selectively used. The spatial prediction using a decision tree can be used not only for spatial predictions, but also for causal analysis in the specific event occurrence location.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.1
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• pp.55-65
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• 2020

With the development of display hardware, image interpolation techniques have been used in various fields such as image zooming and medical imaging. Traditional image interpolation methods, such as bi-linear interpolation, bi-cubic interpolation and edge direction-based interpolation, perform interpolation in the spatial domain. Recently, interpolation techniques in the discrete cosine transform or wavelet domain are also proposed. Using these various existing interpolation methods and machine learning, we propose decision tree classification-based image interpolation methods. In other words, this paper is about the method of adaptively applying various existing interpolation methods, not the interpolation method itself. To obtain the decision model, we used Weka's J48 library with the C4.5 decision tree algorithm. The proposed method first constructs attribute set and select classes that means interpolation methods for classification model. And after training, interpolation is performed using different interpolation methods according to attributes characteristics. Simulation results show that the proposed method yields reasonable performance.

• Journal of Korean Society of Rural Planning
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• v.28 no.4
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• pp.1-9
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• 2022

Rural decline due to the decrease of the local population is an inevitable phenomenon, and a vicious cycle has been formed between a lack of basic living services and a population decrease in rural areas. Therefore, the study aims to derive the migration decision-making characteristics based on basic living service infrastructure data in rural areas. To do this, the population change over the past 20 years was categorized into six types, and the relationship between the classified population change types and the number of basic living service infrastructures was analyzed using decision tree analysis. Of the total 3,501 regions, 801 regions were the continuous decline type, of which 740 were rural areas. On the other hand, among 569 regions that were the continuous increase type, 401 regions were urban areas, confirming the population imbalance between rural and urban areas. As a result of the decision tree analysis on the relationship between population change types and the distribution of basic living service infrastructure, the number of daycare centers was derived as an important variable to classify the continuous increase type. Hospitals, parks, and public transportation were also found to be major basic living services affecting the classification of population change types.

• Geomechanics and Engineering
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• v.36 no.5
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• pp.441-453
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• 2024

Sinkhole subsidence and collapse is a common geohazard often formed in karst areas such as the state of Florida, United States of America. To predict the sinkhole occurrence, we need to understand the formation mechanism of sinkhole and its karst hydrogeology. For this purpose, investigating the factors affecting sinkholes is an essential and important step. The main objectives of the presenting study are (1) the development of a machine learning (ML)-based model, namely C5.0 decision tree (C5.0 DT), for the prediction of sinkhole susceptibility, which accounts for sinkhole/subsidence inventory and sinkhole contributing factors (e.g., geological/hydrogeological) and (2) the construction of a regional-scale sinkhole susceptibility map. The study area is east central Florida (ECF) where a cover-collapse type is commonly reported. The C5.0 DT algorithm was used to account for twelve (12) identified hydrogeological factors. In this study, a total of 1,113 sinkholes in ECF were identified and the dataset was then randomly divided into 70% and 30% subsets for training and testing, respectively. The performance of the sinkhole susceptibility model was evaluated using a receiver operating characteristic (ROC) curve, particularly the area under the curve (AUC). The C5.0 model showed a high prediction accuracy of 83.52%. It is concluded that a decision tree is a promising tool and classifier for spatial prediction of karst sinkholes and subsidence in the ECF area.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.961-968
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• 2018

Recently, researches on automatic recognition of human activities have been actively carried out with the emergence of various intelligent systems. Since a large amount of visual data can be secured through Closed Circuit Television, it is required to recognize human behavior in a dynamic situation rather than a static situation. In this paper, we propose new intelligent human activity recognition model using the trajectory information extracted from the video sequence. The proposed model consists of three steps: segmentation and partitioning of trajectory step, feature extraction step, and behavioral learning step. First, the entire trajectory is fuzzy partitioned according to the motion characteristics, and then temporal features and spatial features are extracted. Using the extracted features, four pedestrian behaviors were modeled by decision tree learning algorithm and performance evaluation was performed. The experiments in this paper were conducted using Caviar data sets. Experimental results show that trajectory provides good activity recognition accuracy by extracting instantaneous property and distinctive regional property.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.2_2
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• pp.333-341
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• 2024

The current public survey performance review extracts samples according to the set screening ratio, and examines the extracted samples to determine the suitability or inadequacy of the survey performance. The examiner directly judges the survey performance submitted by the performer, and extracts it in consideration of various field conditions and topography for each subject. However, it is necessary to secure fairness in the examination as it is extracted with different extraction methods for each subject and the judgment of the examiner. Accordingly, in order to automate sampling for public survey performance review, the detailed sampling criteria of the reviewer were investigated to prepare a volume calculation table, and the automation of sampling using Python was studied. In addition, by reviewing items that can and cannot be automated, the application of the automated decision tree algorithm of sampling was reviewed.

• Journal of KIISE:Software and Applications
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• v.34 no.1
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• pp.18-29
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• 2007

In this paper we present a system that classifies brain MR images by using 2 level decision tree learning. There are two kinds of information that can be obtained from images. One is the low-level features such as size, color, texture, and contour that can be acquired directly from the raw images, and the other is the high-level features such as existence of certain object, spatial relations between different parts that must be obtained through the interpretation of segmented images. Learning and classification should be performed based on the high-level features to classify images according to their semantic meaning. The proposed system applies decision tree learning to each level separately, and the high-level features are synthesized from the results of low-level classification. The experimental results with a set of brain MR images with tumor are discussed. Several experimental results that show the effectiveness of the proposed system are also presented.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.4
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• pp.777-786
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• 2018

This study propose a new method to detect Cochlodinium polykrikoides on satellite images using logistic regression and decision tree. We used spectral profiles(918) extracted from red tide, clear water and turbid water as training data. The 70% of the entire data set was extracted and used for model training, and the classification accuracy of the model was evaluated by using the remaining 30%. As a result of the accuracy evaluation, the logistic regression model showed about 97% classification accuracy, and the decision tree model showed about 86% classification accuracy.