• 정보기술과데이타베이스저널
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• 제6권1호
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• pp.89-101
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• 1999

Increasing number of companies recognize that the understanding of customers and their markets is indispensable for their survival and business success. The companies are rapidly increasing the amount of investments to develop customer databases which is the basis for the database marketing activities. Database marketing is closely related to data mining. Data mining is the non-trivial extraction of implicit, previously unknown and potentially useful knowledge or patterns from large data. Data mining applied to database marketing can make a great contribution to reinforce the company's competitiveness and sustainable competitive advantages. This paper develops the classification model to select the most responsible customers from the customer databases for telemarketing system and evaluates the performance of the developed model using LIFT measure. The model employs the decision tree algorithm, i.e., CHAID which is one of the well-known data mining techniques. This paper also represents the effective database marketing strategy by applying the data mining technique to a credit card company's telemarketing system.

• 한국경영과학회지
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• 제30권4호
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• pp.27-43
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• 2005

This paper presents a new hybrid data mining technique using error pattern modeling to improve classification accuracy when the data type of a target variable is binary. The proposed method increases prediction accuracy by combining two different supervised learning methods. That is, the algorithm extracts a subset of training cases that are predicted inconsistently by both methods, and models error patterns from the cases. Based on the error pattern model, the Predictions of two different methods are merged to generate final prediction. The proposed method has been tested using practical 10 data sets. The analysis results show that the performance of proposed method is superior to the existing methods such as artificial neural networks and decision tree induction.

• 한국인터넷방송통신학회논문지
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• 제20권4호
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• pp.107-113
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• 2020

매우 많은 소프트웨어 결함 예측에 관한 연구들이 수행되어왔지만 대부분은 라벨 데이터를 훈련 데이터로 사용하는 감독형 모델들이었다. 언라벨 데이터만을 사용하는 비감독형 모델이나 언라벨 데이터와 매우 적은 라벨 데이터 정보를 함께 사용하는 세미감독형 모델에 관한 연구는 극소수에 불과하다. 본 논문은 Self-training 기법에 트리 알고리즘들을 사용하여 새로운 세미감독형 모델들을 제작하였다. 세미감독형 기법인 Self-training 모델에 트리 기법들을 사용하는 새로운 세미감독형 모델들을 제작하였다. 모델 평가 실험 결과 새롭게 제작한 트리 모델들이 기존 모델들보다 더 나은 성능을 보였으며, 특히 CollectiveWoods는 타 모델들에 비해 압도적으로 우월한 성능을 보였다. 또한 매우 적은 라벨 데이터 보유 상황에서도 매우 안정적인 성능을 보였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2016년도 학술발표회
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• pp.330-330
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• 2016

The quantification of dry season evaporation in regions, where the magnitude of dry season flows is key to the regional water supply, is essential for good water management. Also, tree transpiration has a significant role in the water balance of a catchment whenever it is tree populated, especially in water limited environments. Such is the case in the Middle Mountains of Nepal where dry season flows play a significant role in downstream water provisioning and their proper functioning is key to the welfare of millions of people. This research seeks to study the transpiration of a pine forest stand in the Jikhu Khola Watershed in the Middle Mountains of Nepal. To the author's knowledge, no single study has been made so far to estimate the dry season evaporation from the planted forest stand in the Middle Mountains of Nepal. The study was carried out in planted pine forest embedded within the Jikhu Khola Catchment. Field campaigns of sap flow measurements were carried out from September, 2010 to February, 2011 in the selected plot of 15*15m dimension, to characterize dry season evaporation. This was done by measuring sap fluxes and sapwood areas over the six trees of different Diameter at Breast Height (DBH) classes. The sap flux was assessed using Granier's thermal dissipation probe (TDP) technique while sapwood area was determined using several incremental core(s) taken with a Pressler borer and immediately dyeing with methyl orange for estimating the actual depth of sapwood area. Transpiration of the plot was estimated by considering the contribution of each tree class. For this purpose, sap flux density, sapwood area and the proportion of total canopy area were determined for each tree class of the selected plot. From these data, hourly and diurnal transpiration rates for the plot were calculated for experimental period. Finally, Cienciala model was parameterized using the data recorded by the ADAS and other terrain data collected in the field. The calibrated model allowed the extrapolation of Sap flux density (v) over a six month period, from September 2010 to February 2011. The model given sap flux density was validated with the measured sap flux density from Grainier method.

• 대한간호학회지
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• 제43권1호
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• pp.1-10
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• 2013

Purpose: The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method. Methods: A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs. Results: The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease. Conclusion: The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

• 한국산학기술학회논문지
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• 제14권7호
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• pp.3400-3411
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• 2013

본 연구의 목적은 회복실 환자의 평균 체류시간을 알아보고, 체류시간에 미치는 요인들을 파악하여 회복실 체류 시간 예측을 위한 분석을 하기 위함이다. 본 연구의 대상자는 상급 종합병원에 입원한 전신 마취 하에 일반외과 수술을 받은 18세 이상 성인 남녀 환자 중 회복실로 입실한 환자를 1,500명을 대상으로 하였고 이중 1,293건을 분석하였다. 회복실 체류시간에 영향을 미치는 요인으로 32항목을 측정하였다. 평균 회복실 체류시간은 72.02분이었다. 수술주기별 관련요인과 회복실 체류시간의 관계를 살펴본 결과 나이, 수술종류, 수술시간, 진통제사용회수가 유의미한 관계를 나타내었다 회복실 체류시간에 가장 영향을 많이 주는 변수는 수술종류이며 그 다음 EKG 이상여부, 나이, 마취제, 수술시간으로 나타났다. 범주 I(30분~60분)은 2개의 경우, 범주 II(61분~90분)도 2개의 경우, 범주 III(91분~120분)은 4개의 경우로 분석되었다.

• 대한공간정보학회지
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• 제5권2호
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• pp.45-54
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• 1997

도시녹지공간의 수목은 체계적이고, 신속한 관리를 함으로써 인간에게 더 많은 혜택을 부여한다. 현재 도시 녹지공간에 있어서의 수목관리의 방법론적인 방안을 고려해 볼 때 수목관리의 유지관리를 위한 자료의 수집과 처리, 첨단 System을 활용한 관리체계가 극히 미흡한 실정이다. 이를 위해서는 먼저 녹지자원 관리를 위한 효율적이고 체계적인 자료의 수집 및 처리와 합리적인 분석과정이 필요하며 그 다음으로 이러한 자료를 토대로 하여 적절한 도시녹지공간에 있어서의 수목관리를 효율적으로 처리할 수 있는 시스템과 그 활용이 필요하다. 이러한 시스템으로 본 연구에서는 최근 여러 분야에서 이용되고 있는 GIS을 이용하여 도시녹지관리모델을 제시하고 이를 통하여 그 효율성을 극대화하였다.

• 정보처리학회논문지D
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• 제15D권1호
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• pp.23-30
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• 2008

유비쿼터스 환경에서 상황정보 인식 분야를 연구하면서 가장 밑바탕에서 기초가 될 수 있는 것은 인터넷 기술과 XML(Extensible Markup Language)이다. 인터넷을 통한 통신에서 XML 데이터의 사용이 일반화되고 있으며 데이터의 형태는 연속적이다. 그리고 XML 스트림 데이터에 대한 질의를 처리하기 위한 방안들이 제시되고 있다. 이 논문에서는 스트림 데이터에 대한 질의처리를 효율적으로 수행하기 위한 기반연구로써 XML을 레이블의 순서화된 트리로 모델링하여 온라인 환경에서 빈발한 구조를 추출하는 마이닝 방법을 제안한다. 즉, 지속적으로 입력되는 XML 데이터의 구조를 트리로 모델링하고 각각의 트리를 하나의 트리 집합의 구조로 표현하여 현재 윈도우 시점에서 빈발한 구조를 정확하고 빠르게 추출하는 방법을 제시한다. 제시하는 방법은 XML의 질의 처리 및 색인 구성의 기초 자료로 활용될 수 있다.

• International journal of advanced smart convergence
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• 제12권1호
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• pp.92-100
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• 2023

Wireless communication technology is becoming increasingly prevalent in smart factories, but the rise in the number of wireless devices can lead to interference in the ISM band and obstacles like metal blocks within the factory can weaken communication signals, creating radio shadow areas that impede information exchange. Consequently, accurately determining the radio communication coverage range is crucial. To address this issue, a Radio Environment Map (REM) can be used to provide information about the radio environment in a specific area. In this paper, a technique for estimating an indoor REM usinga mobile robot and machine learning methods is introduced. The mobile robot first collects and processes data, including the Received Signal Strength Indicator (RSSI) and location estimation. This data is then used to implement the REM through machine learning regression algorithms such as Extra Tree Regressor, Random Forest Regressor, and Decision Tree Regressor. Furthermore, the numerical and visual performance of REM for each model can be assessed in terms of R2 and Root Mean Square Error (RMSE).

• 산업경영시스템학회지
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• 제40권1호
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• pp.57-64
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• 2017

In order to reduce damages to major railroad components, which have the potential to cause interruptions to railroad services and safety accidents and to generate unnecessary maintenance costs, the development of rolling stock maintenance technology is switching from preventive maintenance based on the inspection period to predictive maintenance technology, led by advanced countries. Furthermore, to enhance trust in accordance with the speedup of system and reduce maintenances cost simultaneously, the demand for fault diagnosis and prognostic health management technology is increasing. The objective of this paper is to propose a highly reliable learning model using various machine learning algorithms that can be applied to critical rolling stock components. This paper presents a model for railway rolling stock component fault diagnosis and conducts a mechanical failure diagnosis of motor components by applying the machine learning technique in order to ensure efficient maintenance support along with a data preprocessing plan for component fault diagnosis. This paper first defines a failure diagnosis model for rolling stock components. Function-based algorithms ANFIS and SMO were used as machine learning techniques for generating the failure diagnosis model. Two tree-based algorithms, RadomForest and CART, were also employed. In order to evaluate the performance of the algorithms to be used for diagnosing failures in motors as a critical railroad component, an experiment was carried out on 2 data sets with different classes (includes 6 classes and 3 class levels). According to the results of the experiment, the random forest algorithm, a tree-based machine learning technique, showed the best performance.