• 농촌계획
• /
• 제12권2호
• /
• pp.11-16
• /
• 2006

Value of rural amenity resources scattered in rural area is difficult to be assessed isolatedly. Rural Amenity Resources Map (RARM) is developed for assessing rural resources based on specific territory. In the developing process, knowledge based system is oriented because rural amenity is differed from personal feeling. Possible decision trees in RARM are developed in reviewing process of previous GIS based decision support systems, and information strategy planning is constructed for achieving goals such as rural tourism, down shifting, and rural investment.

• 한국콘텐츠학회:학술대회논문집
• /
• 한국콘텐츠학회 2003년도 춘계종합학술대회논문집
• /
• pp.155-159
• /
• 2003

데이터마이닝 작업의 대표적 방법 중의 하나인 의사결정목의 자료 단편화 및 소집단 자료에 대한 경시성 문제를 보완할 수 있는 방법으로 연관규칙 알고리즘을 활용한 기술적 규칙집합을 찾는 방법을 기술한다. 이를 위해 연관규칙 발견 알고리즘의 원리를 다루고 이를 테이블 형태의 데이터베이스에 효율적으로 적용하는 방법을 기술한다. 아울러 이러한 방법은 원 연관규칙 알고리즘을 이용할 때보다 효율적 작업이 가능함을 실험 데이터에 대한 분석을 통해 살펴보았다.

• International journal of advanced smart convergence
• /
• 제5권1호
• /
• pp.30-33
• /
• 2016

With the advent of big data, data mining is more increasingly utilized in various decision-making fields by extracting hidden and meaningful information from large amounts of data. Even as exponential increase of the request of unrevealing the hidden meaning behind data, it becomes more and more important to decide to select which data mining algorithm and how to use it. There are several mainly used data mining algorithms in biology and clinics highlighted; Logistic regression, Neural networks, Supportvector machine, and variety of statistical techniques. In this paper it is attempted to compare the classification performance of an exemplary algorithm J48 and REPTree of ML algorithms. It is confirmed that more accurate classification algorithm is provided by the performance comparison results. More accurate prediction is possible with the algorithm for the goal of experiment. Based on this, it is expected to be relatively difficult visually detailed classification and distinction.

• 한국정보과학회:학술대회논문집
• /
• 한국정보과학회 2001년도 봄 학술발표논문집 Vol.28 No.1 (B)
• /
• pp.82-84
• /
• 2001

데이터 분류(classification)란 이미 분류된 객체집단군 즉, 학습 데이터에 대한 분석을 바탕으로 아직 분류되지 않는 개체의 소속 집단을 결정하는 작업이다. 현재까지 제안된 여러 가지 분류 모델 중 결정 트리(decision tree)는 인간이 이해하기 쉬운 형태를 갖고 있기 때문에 탐사적인 데이터 마이닝(exploatory)작업에 특히 유용하다. 본 논문에서는 결정 트리 분류에 다중 추상화 수준 문제(multiple abstraction level problem)를 소개하고 이러한 문제를 다루기 위한 실용적인 방법을 제안한다. 데이터의 다중 추상화 수준 문제를 해결하기 위해 추상화 수준을 강제로 같게 하는 것이 문제를 해결할 수 없다는 것을 보인 후, 데이터 값들 사이의 일반화, 세분화 관련성을 그대로 유지하면서 존재하는 유용화할 수 있는 방법을 제시한다.

• Journal of the Korean Data and Information Science Society
• /
• 제27권1호
• /
• pp.255-264
• /
• 2016

Classification is a predictive modeling for a categorical target variable. Various classification ensemble methods, which predict with better accuracy by combining multiple classifiers, became a powerful machine learning and data mining paradigm. Well-known methodologies of classification ensemble are boosting, bagging and random forest. In this article, we assume that decision trees are used as classifiers in the ensemble. Further, we hypothesized that tree size affects classification accuracy. To study how the tree size in uences accuracy, we performed experiments using twenty-eight data sets. Then we compare the performances of ensemble algorithms; bagging, double-bagging, boosting and random forest, with different tree sizes in the experiment.

• 지능정보연구
• /
• 제4권1호
• /
• pp.25-40
• /
• 1998

본 논문에서는 경사진 의사결정 트리(oblique decision tree)에 의해 몇 개의 영역으로 분할된 입력공간(input space)에서 우수한 성능을 발휘할 수 있도록 유전적 프로그래밍 트리들(genetic programming trees)과 연합된 다중 인공신경망 시스템을 개발하였다. 다중 인공신경망인 지역 에이전트들(local agents)은 불할된 영역을 책임지며, 유전적 프로그래밍 트리들로 구성된 경계 에이전트들 (boundary agents)은 불할된 영역의 경계부분만을 담당하게 된다. 본 연방 에이전트 시스템을 이용하여 설계 초기단계의 정보 제한성을 극복하고, 선박 초기설계 단계에서 선박 중앙부 형상설계를 수행하여 범용 설계 시스템으로서의 유용성을 검증하였다.

• Journal of Information Technology Applications and Management
• /
• 제20권2호
• /
• pp.177-191
• /
• 2013

Social network has been expected to increase the value of social capital through online user interactions which remove geographical boundary. However, online users in social networks face challenges of assessing whether the anonymous user and his/her providing information are reliable or not because of limited experiences with a small number of users. Therefore. it is vital to provide a successful trust model which builds and maintains a web of trust. This study aims to propose a prediction method for the interpersonal trust which measures the level of trust about information provider in Facebook. To develop the prediction method. we first investigated behavioral research for trust in social science and extracted 5 antecedents of trust : lenience, ability, steadiness, intimacy, and similarity. Then we measured the antecedents from the history of interactive behavior and built prediction models using the two decision trees and a computational model. We also applied the proposed method to predict interpersonal trust between Facebook users and evaluated the prediction accuracy. The predicted trust metric has dynamic feature which can be adjusted over time according to the interaction between two users.

• 한국전산구조공학회:학술대회논문집
• /
• 한국전산구조공학회 2000년도 가을 학술발표회논문집
• /
• pp.159-165
• /
• 2000

A new state evaluation approach for structural safety is presented in this study. To reduce the subjectivity of the view and judgement of each expert founded on a limited body of knowledge in cognitive and inferential process of safety assessment, we introduced inductive learning method in AI. Inductive learning derives generalization from experiences. Decision tree induction algorithm analyzes the domain knowledge, produce rules via decision trees and then allow us to determine the classification of an object from case examples. The training set of state evaluation is constructed according to the selected attributes from working reports of RC bridge girders.

• ETRI Journal
• /
• 제36권6호
• /
• pp.1016-1022
• /
• 2014

This paper presents a hybrid model solution for user motion recognition. The use of a single classifier in motion recognition models does not guarantee a high recognition rate. To enhance the motion recognition rate, a hybrid model consisting of decision trees and artificial neural networks is proposed. We define six user motions commonly performed in an indoor environment. To demonstrate the performance of the proposed model, we conduct a real field test with ten subjects (five males and five females). Experimental results show that the proposed model provides a more accurate recognition rate compared to that of other single classifiers.

• Communications for Statistical Applications and Methods
• /
• 제13권2호
• /
• pp.449-466
• /
• 2006

We propose to use variable selection methods based on penalized regression for pruning decision tree ensembles. Pruning methods based on LASSO and SCAD are compared with the cluster pruning method. Comparative studies are performed on some artificial datasets and real datasets. According to the results of comparative studies, the proposed methods based on penalized regression reduce the size of boosting ensembles without decreasing accuracy significantly and have better performance than the cluster pruning method. In terms of classification noise, the proposed pruning methods can mitigate the weakness of AdaBoost to some degree.