• 한국통신학회논문지
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• 제24권12B호
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• pp.2447-2454
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• 1999

본 논문은 기존 분류형 신경망의 인식성능을 향상시키기 위하여 프레임 정규화와 비선형 사후확률 추정법(N-APPEM)을 제안하고 한국어 숫자음에 대하여 예측형과 분류형 신경망으로 인식성능을 평가하였다. 실험결과 예측형 신경망에서 최고 98.0%의 인식률을 얻었다. 예측형 신경망은 네트워크가 입력패턴의 카테고리 수만큼 마련되는 복잡한 네트워크를 가지는 반면에 분류형 신경망은 단일 네트워크로 구성되며 프레임 정규화와 비선형 사후확률 추정법으로 85.5%까지 인식률을 향상시킬 수 있었으며 이는 기존의 방법보다 인식률이 12.0% 향상된 것이다.

• International Journal of Internet, Broadcasting and Communication
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• 제11권2호
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• pp.1-10
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• 2019

Research classification of software modules was done to validate the approaches proposed for addressing limitations in existing classification approaches. The objective of this study was to replicate the experiments of a recently published research study and re-evaluate its results. The reason to repeat the experiment(s) and re-evaluate the results was to verify the approach to identify the faulty and non-faulty modules applied in the original study for the prioritization of test cases. As a methodology, we conducted this study to re-evaluate the results of the study. The results showed that binary logistic regression analysis remains helpful for researchers for predictions, as it provides an overall prediction of accuracy in percentage. Our study shows a prediction accuracy of 92.9% for the PureMVC Java open source program, while the original study showed an 82% prediction accuracy for the same Java program classes. It is believed by the authors that future research can refine the criteria used to classify classes of web systems written in various programming languages based on the results of this study.

• 한국IT서비스학회지
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• 제18권1호
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• pp.187-200
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• 2019

Bankruptcy prediction model is an issue that has consistently interested in various fields. Recently, as technology for dealing with unstructured data has been developed, researches applied to business model prediction through text mining have been activated, and studies using this method are also increasing in bankruptcy prediction. Especially, it is actively trying to improve bankruptcy prediction by analyzing news data dealing with the external environment of the corporation. However, there has been a lack of study on which news is effective in bankruptcy prediction in real-time mass-produced news. The purpose of this study was to evaluate the high impact news on bankruptcy prediction. Therefore, we classify news according to type, collection period, and analyzed the impact on bankruptcy prediction based on sentiment analysis. As a result, artificial neural network was most effective among the algorithms used, and commentary news type was most effective in bankruptcy prediction. Column and straight type news were also significant, but photo type news was not significant. In the news by collection period, news for 4 months before the bankruptcy was most effective in bankruptcy prediction. In this study, we propose a news classification methods for sentiment analysis that is effective for bankruptcy prediction model.

• 종양간호연구
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• 제5권1호
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• pp.3-10
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• 2005

Back ground: Nurses now occupy one third of all hospital human resources. Therefore, efficient management of nursing manpower is getting more important. While it is very clear that nursing workload requirement analysis and patient severity classification should be done first for the efficient allocation of nursing workforce, these processes have been conducted manually with ad hoc rule. Purposes: This study was tried to make a predict model for patient classification according to nursing need. We tried to find the easier and faster method to classify nursing patients that can help efficient management of nursing manpower. Methods: The nursing patient classifications data of the hospitalized cancer patients in one of the biggest cancer center in Korea during 2003.1.1-2003.12.31 were assessed by trained nurses. This study developed a prediction model and analyzing nursing needs by data mining techniques. Patients were classified by three different data mining techniques, (Logistic regression, Decision tree and Neural network) and the results were assessed. Results: The data set was created using 165,073 records of 2,228 patients classification database. Main explaining variables were as follows in 3 different data mining techniques. 1) Logistic regression : age, month and section. 2) Decision tree : section, month, age and tumor. 3) Neural network : section, diagnosis, age, sex, metastasis, hospital days and month. Among these three techniques, neural network showed the best prediction power in ROC curve verification. As the result of the patient classification prediction model developed by neural network based on nurse needs, the prediction accuracy was 84.06%. Conclusion: The patient classification prediction model was developed and tested in this study using real patients data. The result can be employed for more accurate calculation of required nursing staff and effective use of labor force.

• 대한음성학회지:말소리
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• 제57호
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• pp.139-152
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• 2006

In this paper, we present a phrase break prediction method using CRF(Conditional Random Fields), which has good performance at classification problems. The phrase break prediction problem was mapped into a classification problem in our research. We trained the CRF using the various linguistic features which was extracted from POS(Part Of Speech) tag, lexicon, length of word, and location of word in the sentences. Combined linguistic features were used in the experiments, and we could collect some linguistic features which generate good performance in the phrase break prediction. From the results of experiments, we can see that the proposed method shows improved performance on previous methods. Additionally, because the linguistic features are independent of each other in our research, the proposed method has higher flexibility than other methods.

• ETRI Journal
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• 제28권6호
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• pp.807-810
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• 2006

This letter presents a prediction model for sentence-final intonations for Korean conversational-style text-to-speech systems in which we introduce the linguistic feature of 'modality' as a new parameter. Based on their function and meaning, we classify tonal forms in speech data into tone types meaningful for speech synthesis and use the result of this classification to build our prediction model using a tree structured classification algorithm. In order to show that modality is more effective for the prediction model than features such as sentence type or speech act, an experiment is performed on a test set of 970 utterances with a training set of 3,883 utterances. The results show that modality makes a higher contribution to the determination of sentence-final intonation than sentence type or speech act, and that prediction accuracy improves up to 25% when the feature of modality is introduced.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2226-2229
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• 2003

A new method is presented to construct a classifier. This was accomplished by combining a generalized regression neural network (GRNN) and a genetic algorithm (GA). The classifier constructed in this way is referred to as a GA-GRNN. The GA played a role of controlling training factors simultaneously. In GA optimization, neuron spreads were represented in a chromosome. The proposed optimization method was applied to a data set, consisted of 4 different promoter sequences. The training and test data were composed of 115 and 58 sequence patterns, respectively. The range of neuron spreads was experimentally varied from 0.4 to 1.4 with an increment of 0.1. The GA-GRNN was compared to a conventional GRNN. The classifier performance was investigated in terms of the classification sensitivity and prediction accuracy. The GA-GRNN significantly improved the total classification sensitivity compared to the conventional GRNN. Also, the GA-GRNN demonstrated an improvement of about 10.1% in the total prediction accuracy. As a result, the proposed GA-GRNN illustrated improved classification sensitivity and prediction accuracy over the conventional GRNN.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1996년도 추계학술대회발표논문집; 고려대학교, 서울; 26 Oct. 1996
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• pp.199-202
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• 1996

Although numerous studies demonstrate that one technique outperforms the others for a given data set, there is often no way to tell a priori which of these techniques will be most effective to solve a specific problem. Alternatively, it has been suggested that a better approach to classification problem might be to integrate several different forecasting techniques by combining their results. The issues of interest are how to integrate different modeling techniques to increase the prediction performance. This paper proposes the post-model integration method, which means integration is performed after individual techniques produce their own outputs, by finding the best combination of the results of each method. To get the optimal or near optimal combination of different prediction techniques. Genetic Algorithms (GAs) are applied, which are particularly suitable for multi-parameter optimization problems with an objective function subject to numerous hard and soft constraints. This study applied three individual classification techniques (Discriminant analysis, Logit and Neural Networks) as base models to the corporate failure prediction context. Results of composite prediction were compared to the individual models. Preliminary results suggests that the use of integrated methods will offer improved performance in business classification problems.

• 한국정보통신학회논문지
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• 제18권10호
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• pp.2562-2570
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• 2014

단백질의 기능을 유추할 수 있는 중요한 정보중의 하나는 단백질이 존재하는 세포내 위치이다. 최근에는 하나의 단백질이 동시에 존재하는 여러 세포내 위치를 예측하는 연구가 활발하다. 본 논문에서는 단백질이 존재하는 세포내의 다중위치를 예측하기 위해서 레이블 멱집합 방법을 개선한다. 레이블 멱집합 방법으로 분류한 다중위치들을 예측 확률에 따라 결합하여 최종적인 다중레이블로 분류한다. 각 다중위치에 대한 정확한 확률적 기여를 구하기 위하여 쌍별 비교와 오류정정 출력코드를 사용한 다중클래스 확률추정 방법을 적용하였다. 단백질 세포내 위치 예측 실험에 제안한 방법을 적용하여 성능이 향상됨을 보였다.

• 한국터널지하공간학회 논문집
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• 제18권2호
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• pp.245-256
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• 2016

TBM을 적용하는 현장에서 장비 선택, 공사기간 및 공사비용의 합리적인 산정을 위하여 TBM의 굴진성능을 정확하게 예측하는 것은 매우 중요한 사안이다. 본 연구에서는 최신 자료들을 바탕으로 기존의 TBM 굴진성능 예측모델들의 평가과정과 방법론에 대한 분석을 수행하였다. 2000년 이후에 발표된 문헌들에 대한 조사를 토대로 TBM 굴진성능 예측모델의 분류체계를 제시하였다. 본 연구에서 제시한 분류체계에서는 TBM 굴진성능 예측모델에 필요한 입력인자 선정단계와 예측기법 적용단계로 크게 구분하였다. 또한 각 예측모델에서 사용된 입력인자, 출력인자 그리고 예측모델에서 사용된 인자의 적용빈도를 분석하였다. 마지막으로 TBM 굴진성능 예측모델의 현황과 향후 연구방향에 대하여 제언하였다.