• Geomechanics and Engineering
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• 제36권5호
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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.

• 경영정보학연구
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• 제8권3호
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• pp.125-134
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• 2006

본 연구에서는 온라인 자동차보험 고객 이탈 예측에 있어 의사결정나무를 적용하였다. 우리는 본 연구에서 2003년과 2004년 사이에 온라인 자동차 보험을 계약한 고객의 데이터를 이용하여 의사결정나무를 이용해 고객이탈을 예측하였다. 우리는 C5.0 알고리즘에 기반을 둔 의사결정나무의 예측 결과에 대한 비교를 위해 다변량판별분석과 로짓분석을 이용하였다. 분석결과 의사결정나무 알고리즘은 다른 기법보다 예측성과가 매우 뛰어난 것으로 나타났다. 이러한 실증분석 결과는 온라인 자동차 보험에 있어서 마케팅전략 수립에 유용한 가이드라인을 제공해 줄 것이다.

• International Journal of Contents
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• 제13권2호
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• pp.57-65
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• 2017

Recently, due to the significance of Industry 4.0, the manufacturing industry is developing globally. Conventionally, the manufacturing industry generates a large volume of data that is often related to process, line and products. In this paper, we analyzed causes of defective products in the manufacturing process using the decision tree technique, that is a well-known technique used in data mining. We used data collected from the domestic manufacturing industry that includes Manufacturing Execution System (MES), Point of Production (POP), equipment data accumulated directly in equipment, in-process/external air-conditioning sensors and static electricity. We propose to implement a model using C4.5 decision tree algorithm. Specifically, the proposed decision tree model is modeled based on components of a specific part. We propose to identify the state of products, where the defect occurred and compare it with the generated decision tree model to determine the cause of the defect.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2004년도 추계학술대회
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• pp.63-75
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• 2004

The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud dection, data reduction and variable screening, category merging, etc. We analyze Gyeongnam social indicator survey data using decision tree techniques for environmental information. We can use these decision tree outputs for environmental preservation and improvement.

• Journal of the Korean Data and Information Science Society
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• 제15권4호
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• pp.759-771
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• 2004

The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud dection, data reduction and variable screening, category merging, etc. We analyze Gyeongnam social indicator survey data using decision tree techniques for environmental information. We can use these decision tree outputs for environmental preservation and improvement.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 춘계학술대회
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• pp.164-173
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• 2005

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud detection, data reduction and variable screening, category merging, etc. We analyze waste database united with local information using decision tree techniques for environmental information. We can use these decision tree outputs for environmental preservation and improvement.

• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.845-857
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• 2003

We explore the use of genetic programming to evolve decision trees directly for classification problems with both discrete and continuous predictors. We demonstrate that the derived hypotheses of standard algorithms can substantially deviated from the optimum. This deviation is partly due to their top-down style procedures. The performance of the system is measured on a set of real and simulated data sets and compared with the performance of well-known algorithms like CHAID, CART, C5.0, and QUEST. Proposed algorithm seems to be effective in handling problems caused by top-down style procedures of existing algorithms.

• 품질경영학회지
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• 제46권3호
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• pp.739-752
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• 2018

Purpose: The purpose of this study was to investigate the relationship between kiln processing parameters and NOx emissions that occur in the sintering and calcination steps of the cement manufacturing process and to derive the main factors responsible for producing emissions outside emission limit criteria, as determined by category models and classification rules, using data mining techniques. The results from this study are expected to be useful as guidelines for NOx emission control standards. Methods: Data were collected from Precalciner Kiln No.3 used in one of the domestic cement plants in Korea. Thirty-four independent variables affecting NOx generation and dependent variables that exceeded or were below the NOx emiision limit (>1 and <0, respectively) were examined during kiln processing. These data were used to construct a detection model of NOx emission, in which emissions exceeded or were below the set limits. The model was validated using SPSS MODELER 18.0, artificial neural network, decision treee (C5.0), and logistic regression analysis data mining techniques. Results: The decision tree (C5.0) algorithm best represented NOx emission behavior and was used to identify 10 processing variables that resulted in NOx emissions outside limit criteria. Conclusion: The results of this study indicate that the decision tree (C5.0) can be applied for real-time monitoring and management of NOx emissions during the cement firing process to satisfy NOx emission control standards and to provide for a more eco-friendly cement product.

• 정보처리학회논문지C
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• 제14C권1호
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• pp.1-6
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• 2007

안전한 네트워크의 운영을 함에 있어 네트워크 침입 탐지에서 오탐지율을 줄이고 정탐지율을 높이는 것은 매우 중요한 일이라 할 수 있다. 최근에 얼굴 인식과 생물학 정보칩 분류 등에서 활발히 적용 연구되는 SVM을 침입탐지에 이용하면 실시간 탐지가 가능하므로 탐지율의 향상을 기대할 수 있다. 그러나 기존의 연구에서는 입력값들을 벡터공간에 나타낸 후 계산된 값을 근거로 분류하므로, 이산형의 데이터는 입력 정보로 사용할 수 없다는 단점을 가지고 있다. 따라서 이 논문에서는 의사결정트리를 SVM에 결합시킨 침입 탐지 모델을 제안하고 이에 대한 성능을 평가한 결과 기존 방식에 비해 침입 탐지율, F-P오류율, F-N오류율에 있어 각각 5.5%, 0.16%, 0.82% 향상이 있음을 보였다.

• 한국정보통신학회논문지
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• 제22권8호
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• pp.1055-1060
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• 2018

4차 산업혁명을 맞이하여 학교 교육에 있어서 진로교육의 문제가 크게 대두되고 있다. 일선 현장에서도 인공지능 및 빅 데이터들을 효과적으로 처리하기 위한 서비스 또는 기술에 대하여 다양한 연구가 진행되고 있으나, 교육분야에 있어서는 학생들에 대한 데이터들을 단순처리과정을 거칠 뿐이다. 이에 본 논문에서는 인공지능 및 빅데이터를 활용한 학생들의 진로교육을 위한 진로 예측 프로그램을 설계 제시하고자 한다. 영재교육원 학생들의 관찰데이터를 이용하여 의사결정 트리중 가장 인공지능에 가깝고 효과적이라고 알려진 C4.5알고리즘으로 의사결정 트리를 구성하고 학생들의 희망 진로를 예측하는 것이다. 판별결과 카파계수는 0.7을 넘어 상당한 일치도를 보였고 평균절대오차도 0.1정도로 상당히 낮은 수치를 보였다. 이에 따라서 본 연구에서 보이듯이 많은 연구 및 데이터를 구축하여 학생들의 상담에 활용 진로를 제시하고 수업태도 및 방향을 제시하는데 도움이 될 것으로 사료된다.