• Journal of Convergence for Information Technology
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• v.10 no.7
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• pp.14-19
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• 2020

This study suggests an accounting information processing model based on internet standard XBRL which applies an extensible business reporting language, the XML technology. Due to the differences in document characteristics among various companies, this is very important with regard to the purpose of accounting that the system should provide useful information to the decision maker. This study develops a data mining model based on XML hierarchy which is stored as XBRL in the X-Hive data base. The data ming analysis is experimented by the data mining association rule. And based on XBRL, the DC-Apriori data mining method is suggested combining Apriori algorithm and X-query together. Finally, the validity and effectiveness of the suggested model is investigated through experiments.

• Journal of the Korea Safety Management & Science
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• v.7 no.5
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• pp.1-8
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• 2005

The consequences of rapid industrial advancement, diversified types of business and unexpected industrial accidents have caused a lot of damage to many unspecified persons both in a human way and a material way Although various previous studies have been analyzed to prevent industrial accidents, these studies only provide managerial and educational policies using frequency analysis and comparative analysis based on data from past industrial accidents. The main objective of this study is to find an optimal algorithm for data analysis of industrial accidents and this paper provides a comparative analysis of 4 kinds of algorithms including CHAID, CART, C4.5, and QUEST. Decision tree algorithm is utilized to predict results using objective and quantified data as a typical technique of data mining. Enterprise Miner of SAS and AnswerTree of SPSS will be used to evaluate the validity of the results of the four algorithms. The sample for this work chosen from 19,574 data related to construction industries during three years ($2002\sim2004$) in Korea.

• Korean Journal of Oriental Medicine
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• v.17 no.2
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• pp.121-127
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• 2011

Objectives : In the clinical field, it is important to understand the factors that have effects on a certain disease or symptom. For this, many researchers apply Data Mining method to the clinical data that they have collected. One of the efficient methods for Data Mining is decision tree induction. Many researchers have studied to find the best split criteria of decision tree; however, various split criteria coexist. Methods : In this paper, we applied several split criteria(Information Gain, Gini Index, Chi-Square) to Sasang constitutional clinical information and compared each decision tree in order to find optimal split criteria. Results & Conclusion : We found BMI and body measurement factors are important factors to Sasang constitution by analyzing produced decision trees with different split measures. And the decision tree using information gain had the highest accuracy. However, the decision tree that produced highest accuracy is changed depending on given data. So, researcher have to try to find proper split criteria for given data by understanding attribute of the given data.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.1050-1053
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• 2014

웹 기반 서비스의 발전과 스마트 기기의 보급으로 사용자들은 다양한 웹 서비스들을 이용할 수 있게 되었고, 소셜 웹과 같은 사람들 간의 관계를 형성함으로써 정보를 주고받는 서비스에 접근하여 자신만의 콘텐츠를 생성, 공유하기가 용이해졌다. 그러나 소셜 웹 사용자들이 증가하고 지식의 양이 늘어남에 따라, 방대한 양의 지식들 중 필요한 정보만을 효율적으로 창출해내고자 하는 연구 또한 시도되어 왔다. 그러나, 기존의 방법은 다수의 서비스 사용자들의 공통적인 관심사가 반영된 결과를 도출해내기에는 부족하다는 단점이 있었다. 그리하여, 본 논문에서는 집단 지성 알고리즘과 의사 결정 나무를 활용하여 소셜 웹을 이용하는 사용자들의 태그와 URL 정보를 토대로 트렌드를 분석, 콘텐츠를 추천하는 방법을 제안하고, 이를 통하여 다수 사용자들의 기호가 반영된 다양한 정보들을 소셜 웹 사용자들에게 제공해줄 수 있음을 보인다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.15-23
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• 2021

Most of the open-source decision tree algorithms are based on three splitting criteria (Entropy, Gini Index, and Gain Ratio). Therefore, the advantages and disadvantages of these three popular algorithms need to be studied more thoroughly. Comparisons of the three algorithms were mainly performed with respect to the predictive performance. In this work, we conducted a comparative experiment on the splitting criteria of three decision trees, focusing on their interpretability. Depth, homogeneity, coverage, lift, and stability were used as indicators for measuring interpretability. To measure the stability of decision trees, we present a measure of the stability of the root node and the stability of the dominating rules based on a measure of the similarity of trees. Based on 10 data collected from UCI and Kaggle, we compare the interpretability of DT (Decision Tree) algorithms based on three splitting criteria. The results show that the GR (Gain Ratio) branch-based DT algorithm performs well in terms of lift and homogeneity, while the GINI (Gini Index) and ENT (Entropy) branch-based DT algorithms performs well in terms of coverage. With respect to stability, considering both the similarity of the dominating rule or the similarity of the root node, the DT algorithm according to the ENT splitting criterion shows the best results.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.6_2
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• pp.891-897
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• 2021

In this paper, we propose a destination optimal route algorithm for providing route finding service for the transportation handicapped by using the multi-criteria decision-making technique and the modified A-STAR optimal route search algorithm. This is a method to set the route to the destination centering on safety by replacing the distance cost of the existing A-STAR optimal route search algorithm with the safety cost calculated through AHP/TOPSIS analysis. To this end, 10 factors such as road damage, curb, and road hole were first classified as poor road factors that hinder road driving, and then pairwise comparison of AHP was analyzed and then defined as the weight of TOPSIS. Afterwards, the degree of driving safety was quantified for a certain road section in Busan through TOPSIS analysis, and the development of an optimal route search algorithm for the transportation handicapped that replaces the distance cost with safety in the finally modified A-STAR optimal route algorithm was completed.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.61-64
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• 2021

2016, 2017년 경주와 포항에서 발생한 규모 5.4 이상의 지진 당시 건물에 많은 피해가 속출함에 따라 지진 발생 시 건물 안전에 관한 관심이 증가하고 있다. 이러한 이유로 지진 등의 재난 상황 시 건물의 위험도를 신속하게 판단할 수 있는 방법론이 필요한 실정이다. 본 논문에서는 지진 등의 재난 상황 시 건물 안전에 위협이 될 수 있는 건물 기울기에 대한 위험도를 자이로 센서 데이터에 기반해 산출하는 시스템을 제안한다. 본 논문에서는건물 기울어짐 데이터를 확보함에 어려움이 있어 모의 거동 환경을 구축하여 데이터를 수집 및 분석하였다. 제안된 시스템은 자이로 센서로부터 수집된 실시간 기울기 데이터를 Mean Filter를 통해 데이터 평탄화 및 선형화를 수행 후 머신러닝 기법중 하나인 선형 회귀 알고리즘을 적용해 건물 기울기를 추정한다. 이후 국토교통부에서 고시한 건물 기울기 위험도 산출표를 바탕으로 측정된 기울기의 위험도를 산출한다. 해당 시스템은 실제 지진 등의 재난 발생 시 실시간 건물 기울기 위험 판단을 통해 신속한 재난 의사 결정에 도움이 될 것으로 기대된다.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.243-248
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• 2018

The social activities of the elderly are important in successfully achieving aging by providing opportunities for social interaction to enhance life satisfaction. The purpose of this study is to identify the related factors of the elderly social activities and build a statistical classification model to predict social activities. Subjects were 1,864 elderly people (829 males, 1,035 females) who completed the community health survey in 2015. Outcome variables were defined as the experience of social activity during the past month(yes, no). The prediction model was constructed using decision tree model based on Classification and Regression Trees (CRT) algorithm. The results of this study were subjective health, frequency of meeting with neighbors, frequency of meeting with relatives, and living with spouse were significant variables of social participation. The most prevalent predictor was the subjective health level. In order to prepare for the successful aging of the super aged society based on the results of this study, social attention and support for the social activities of the elderly are required.

• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.5
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• pp.575-589
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• 2020

In recent years, Shield TBM construction has been continuously increasing in domestic tunnels. The main excavation tool in the shield TBM construction is a disc cutter which naturally wears during the excavation process and significantly degrades the excavation efficiency. Therefore, it is important to know the appropriate time of the disc cutter replacement. In this study, it is proposed a predictive model that can determine yes/no of disc cutter replacement using machine learning algorithm. To do this, the shield TBM machine data which is highly correlated to the disc cutter wears and the disc cutter replacement from the shield TBM field which is already constructed are used as the input data in the model. Also, the algorithms used in the study were the support vector machine, k-nearest neighbor algorithm, and decision tree algorithm are all classification methods used in machine learning. In order to construct an optimal predictive model and to evaluate the performance of the model, the classification performance evaluation index was compared and analyzed.