• Journal of the Korean Data and Information Science Society
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• 제22권1호
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• pp.99-105
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• 2011

We propose a semi-supervised learning algorithm based on a form of regularization that incorporates similarity and dissimilarity penalty terms. Our approach uses a graph-based encoding of similarity and dissimilarity. We also present a model-selection method which employs cross-validation techniques to choose hyperparameters which affect the performance of the proposed method. Simulations using two types of dat sets demonstrate that the proposed method is promising.

• 한국IT서비스학회지
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• 제8권4호
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• pp.129-140
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• 2009

The accurate estimation of software reliability is important to a successful development in software engineering. Until recent days, the models using regression analysis based on statistical algorithm and machine learning method have been used. However, this paper estimates the software reliability using support vector regression, a sort of machine learning technique. Also, it finds the best set of optimized parameters applying immune algorithm, changing the number of generations, memory cells, and allele. The proposed IA-SVR model outperforms some recent results reported in the literature.

• International Journal of Advanced Culture Technology
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• 제10권4호
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• pp.499-510
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• 2022

This study examines the reporting factors of crime against business in Korea and proposes a corresponding predictive model using machine learning. While many previous studies focused on the individual factors of theft victims, there is a lack of evidence on the reporting factors of crime against a business that serves the public good as opposed to those that protect private property. Therefore, we proposed a crime prevention model for the willingness factor of theft reporting in businesses. This study used data collected through the 2015 Commercial Crime Damage Survey conducted by the Korea Institute for Criminal Policy. It analyzed data from 834 businesses that had experienced theft during a 2016 crime investigation. The data showed a problem with unbalanced classes. To solve this problem, we jointly applied the Synthetic Minority Over Sampling Technique and the Tomek link techniques to the training data. Two prediction models were implemented. One was a statistical model using logistic regression and elastic net. The other involved a support vector machine model, tree-based machine learning models (e.g., random forest, extreme gradient boosting), and a stacking model. As a result, the features of theft price, invasion, and remedy, which are known to have significant effects on reporting theft offences, can be predicted as determinants of such offences in companies. Finally, we verified and compared the proposed predictive models using several popular metrics. Based on our evaluation of the importance of the features used in each model, we suggest a more accurate criterion for predicting var.

• Journal of Information Processing Systems
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• 제13권4호
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• pp.778-804
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• 2017

Software today has become an inseparable part of our life. In order to achieve the ever demanding needs of customers, it has to rapidly evolve and include a number of changes. In this paper, our aim is to study the relationship of object oriented metrics with change proneness attribute of a class. Prediction models based on this study can help us in identifying change prone classes of a software. We can then focus our efforts on these change prone classes during testing to yield a better quality software. Previously, researchers have used statistical methods for predicting change prone classes. But machine learning methods are rarely used for identification of change prone classes. In our study, we evaluate and compare the performances of ten machine learning methods with the statistical method. This evaluation is based on two open source software systems developed in Java language. We also validated the developed prediction models using other software data set in the same domain (3D modelling). The performance of the predicted models was evaluated using receiver operating characteristic analysis. The results indicate that the machine learning methods are at par with the statistical method for prediction of change prone classes. Another analysis showed that the models constructed for a software can also be used to predict change prone nature of classes of another software in the same domain. This study would help developers in performing effective regression testing at low cost and effort. It will also help the developers to design an effective model that results in less change prone classes, hence better maintenance.

• 산업경영시스템학회지
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• 제38권1호
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• pp.74-82
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• 2015

Two key challenges in statistical relational learning are uncertainty and complexity. Standard frameworks for handling uncertainty are probability and first-order logic respectively. A Markov logic network (MLN) is a first-order knowledge base with weights attached to each formula and is suitable for classification of dataset which have variables correlated with each other. But we need domain knowledge to construct first-order logics and a computational complexity problem arises when calculating weights of first-order logics. To overcome these problems we suggest a method to generate first-order logics and learn weights using association analysis in this study.

• Communications for Statistical Applications and Methods
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• 제31권3호
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• pp.279-289
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• 2024

Familial searching is a useful technique in a forensic investigation. Using genetic information, it is possible to identify individuals, determine familial relationships, and obtain racial/ethnic information. The total number of shared alleles (TNSA) and likelihood ratio (LR) methods have traditionally been used, and novel data-mining classification methods have recently been applied here as well. However, it is difficult to apply these methods to identify familial relationships above the third degree (e.g., uncle-nephew and first cousins). Therefore, we propose to apply a stacking ensemble machine learning algorithm to improve the accuracy of familial relationship identification. Using real data analysis, we obtain superior relationship identification results when applying meta-classifiers with a stacking algorithm rather than applying traditional TNSA or LR methods and data mining techniques.

• 대한수학교육학회지:수학교육학연구
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• 제8권2호
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• pp.453-474
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• 1998

This study is focused on the possibility if we can use graphic calculators in teaching and learning school mathematics. This study is consisted with four main chapters. In chapter II, the functions of the graphic calculator EL-9600 produced by Sharp Corporation was analyzed focused on the possibilities if the functions could be used in teaching and learning school mathematics. Calculating of real numbers and complex numbers, solving equations and system of linear equations, calculating of matrices, graphing of several functions including polynomial functions, trigonometric functions, exponential and logarithmic functions, calculation of differential and integrals, arranging of statical data, graphing of statistical data, testing of statistical hypotheses, and other more useful functions were founded. In Chapter III, a mathematics textbook developed by Core-Plus Mathematics Project was analyzed focused on how a graphic calculator was used in teaching and learning mathematics, In the textbook, graphic calculator was used as a tool in understanding mathematical concepts and solving problems. Graphic calculator is not just a tool to do complex computations but a tool used in the processes of doing mathematics, In chapter IV, the 7th mathematics curriculum for korean secondary schools was analyzed to find the contents could be taught by using graphic calculators. Most of the domains, except geometric figure, were found that they could be taught by using graphic calculators, In chapter V, a model of a unit using graphic calculator in teaching 7th mathematics curriculum was developed. In this model, graphic calculator was used as a tool in the processes of understanding mathematical concepts and solving problems. This study suggests the possibilities that we can use graphic calculators effectively in teaching and learning mathematical concepts and problem solving for most domains of secondary school mathematics.

• 디지털융복합연구
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• 제11권12호
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• pp.319-326
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• 2013

소프트웨어의 디버깅 오류의 발생 시간에 의존하는 많은 소프트웨어 신뢰성 모델이 연구되었다. 소프트웨어 오류 탐색 기법은 사전에 알지 못하지만 자동적으로 발견되는 에러를 고려한 영향요인과 사전 경험에 의하여 세밀하게 에러를 발견하기 위하여 테스팅 관리자가 설정해놓은 요인인 학습효과의 특성에 대한 문제를 비교 제시 하였다. 본 연구에서는 학습효과 비동질적인 유한고장모형 분석을 위한 모수 추정은 우도함수를 이용하였다. 소프트웨어 시장에 인도하기 위한 결정에 대하여 조건부 고장률은 중요한 변수가 되고 이러한 고장 모델은 실제 상황에서 많이 사용되고 있다. 통계적 공정 관리 (SPC)는 소프트웨어 오류의 예측을 모니터링 함으로써 소프트웨어의 신뢰성 향상에 크게 기여할 수 있다. 이러한 컨트롤 차트는 널리 소프트웨어 산업의 소프트웨어 프로세스 제어를 위해 사용된다. 본 연구에서는 로그 위험 학습 효과 속성의 비동질적인 포아송 과정의 평균값 기능을 사용한 컨트롤 메커니즘을 제안하였다.

• Journal of the Korean Data and Information Science Society
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• 제22권2호
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• pp.197-206
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• 2011

신호처리 관련 응용문제에서는 신호에서 실시간으로 발생하는 비정상적인 사건들을 탐지하는 것이 매우 중요하다. 이전에 알려져 있는 비정상 사건 탐지방법들은 신호에 대한 명확한 통계적인 모형을 가정하고, 비정상적인 신호들은 통계적인 모형의 가정 하에서 비정상적인 사건들로 해석한다. 탐지방법으로 최대우도와 베이즈 추정 이론이 많이 사용되고 있다. 그러나 앞에서 언급한 방법으로는 로버스트 하고 다루기 쉬운 모형을 추정한다는 것은 쉽지가 않다. 좀 더 로버스트한 모형을 추정할 수 있는 방법이 필요하다. 본 논문에서는 로버스트 하다고 알려져 있는 서포트 벡터 기계를 이용하여 온라인으로 비정상적인 신호를 탐지하는 방법을 제안한다.

• 산업융합연구
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• 제21권7호
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• pp.29-39
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• 2023

최근 국내‧외적으로 많은 지진이 발생하고 있는 상황에서, 우리나라의 건물은 내진설계 및 지진피해에 매우 취약한 상황이다. 따라서 현 연구의 목적은 건물에 대한 지진취약도 등급화 및 위험건물 밀도분석을 수행하는 효과적인 방법을 발굴하고 이를 모델화하여, 시범지역(서울시)자료를 활용해 검증해 보는데 있다. 이를 위해 활용된 두 가지 모델링 기법 중, 통계 분석 기법의 예측정확도는 87%였고, 머신러닝 기법은 Random Forest모델의 예측정확도가 가장 높았으며, 해당 모델의 Test Set 정확도는 97.1%로 도출되었다. 분석결과, 구별 등급화 결과는 광진구와 송파구가 상대적으로 위험하다고 예측되었으며, 위험건물 밀도분석은 서초구, 관악구, 강서구가 상대적으로 위험하다고 예측되었다. 최종적으로, 통계분석 기법을 활용한 분석결과가 머신러닝 기법을 활용한 분석결과보다 위험하게 도출되었으나, 우리나라에서는 지진 강도 6.5(MMI)가 내진설계의 기준인데, 서울시 건물의 약 18.9%가 내진설계 되어있는 것으로 확인된 것을 고려하면, 머신러닝 기법의 결과가 더 정확할 것으로 예측되었다. 현 연구는 인구 및 인프라와 경찰서, 소방서 등을 고려 않은 오직 건물만을 고려한 한계점이 있으며, 해당 한계를 포함해 수행하면 더욱 포괄적인 연구가 될 것이다.