• 한국경영과학회지
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• 제39권4호
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• pp.75-84
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• 2014

In this paper, we dealt with feature selection problem of large-scale and high-dimensional biological data such as omics data. For this problem, most of the previous approaches used simple score function to reduce the number of original variables and selected features from the small number of remained variables. In the case of methods that do not rely on filtering techniques, they do not consider the interactions between the variables, or generate approximate solutions to the simplified problem. Unlike them, by combining set covering and clustering techniques, we developed a new method that could deal with total number of variables and consider the combinatorial effects of variables for selecting good features. To demonstrate the efficacy and effectiveness of the method, we downloaded gene expression datasets from TCGA (The Cancer Genome Atlas) and compared our method with other algorithms including WEKA embeded feature selection algorithms. In the experimental results, we showed that our method could select high quality features for constructing more accurate classifiers than other feature selection algorithms.

• 한국과학교육학회지
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• 제13권2호
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• pp.152-162
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• 1993

The high school students' problem solving patterns and their features in scientific inquiry, especially on controlling variables and stating hypothesis have been investigated. The 8 problems on controlling variables and stating hypothesis were selected out of the scientific inquiry area in the experimental tryout of Aptitude Assessment for College Education, and had been used to find the patterns and their features. The results of findings are as follows: There were seven patterns in the process of solving problems. Five of seven patterns were found in right answers and four patterns in wrong answers. Two patterns were found in both right and wrong answers. Some students could solve the problems even though they did not understand the elements of the scientific inquiry, controlling variables and stating hypothesis. The false application of physics concepts, misunderstanding about the elements of the scientific inquiry and using unrelated experience and conjectures were the features of students' wrong answers. On the other hand, the right application of physics concepts, understanding and applying the elements right, infering answers from the tables and figures on statements of suggested problems were the features of right answers. The further studies on this kind may helpful to find the higher mental abilities related to scientific inquiry and to develop tools for testing students' scientific inquiry thinking skills.

• Journal of information and communication convergence engineering
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• 제11권3호
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• pp.179-184
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• 2013

Traditional clustering methods are usually based on the bag-of-words (BOW) model. A disadvantage of the BOW model is that it ignores the semantic relationship among terms in the data set. To resolve this problem, ontology or matrix factorization approaches are usually used. However, a major problem of the ontology approach is that it is usually difficult to find a comprehensive ontology that can cover all the concepts mentioned in a collection. This paper proposes a new document clustering method using semantic features and fuzzy relations for solving the problems of ontology and matrix factorization approaches. The proposed method can improve the quality of document clustering because the clustered documents use fuzzy relation values between semantic features and terms to distinguish clearly among dissimilar documents in clusters. The selected cluster label terms can represent the inherent structure of a document set better by using semantic features based on non-negative matrix factorization, which is used in document clustering. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.

• 대한의용생체공학회:의공학회지
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• 제42권6호
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• pp.287-294
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• 2021

Cardiotoxicity assessment of all drugs has been performed according to the ICH guidelines since 2005. Non-clinical evaluation S7B has focused on the hERG assay, which has a low specificity problem. The comprehensive in vitro proarrhythmia assay (CiPA) project was initiated to correct this problem, which presented a model for classifying the Torsade de pointes (TdP)-induced risk of drugs as biomarkers calculated through an in silico ventricular model. In this study, we propose a TdP-induced risk group classifier of artificial neural network (ANN)-based. The model was trained with 12 drugs and tested with 16 drugs. The ANN model was performed according to nine features, seven features, five features as an individual ANN model input, and the model with the highest performance was selected and compared with the classification performance of the qNet input logistic regression model. When the five features model was used, the results were AUC 0.93 in the high-risk group, AUC 0.73 in the intermediate-risk group, and 0.92 in the low-risk group. The model's performance using qNet was lower than the ANN model in the high-risk group by 17.6% and in the low-risk group by 29.5%. This study was able to express performance in the three risk groups, and it is a model that solved the problem of low specificity, which is the problem of hERG assay.

• 한국정보과학회논문지:소프트웨어및응용
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• 제35권10호
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• pp.608-615
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• 2008

불특정 다수 사용자가 정보를 생성하는 지식 질의응답 서비스에서는 문서의 품질이 검색결과 만족도에 중요한 요소 중 하나이다. 지식 질의응답 문서의 품질 평가에 관한 기존 연구는 조회 수와 추천 수 등의 비텍스트 정보를 이용하여 문서의 품질을 평가하고, 이를 검색 모형에 반영하여 검색 성능을 높이는데 집중하였다. 이러한 비텍스트 정보는 그 유용성이 실험을 통해 증명되었다. 그러나 비텍스트 정보를 이용하여 새로 작성된 문서의 품질을 평가할 경우 심각한 자료 부족 문제가 발생할 수 있다는 단점이 있다. 본 논문에서는 이러한 비텍스트 정보의 자료 부족 문제를 완화할 수 있는 새로운 문서 품질 평가자질로서 문서 내용 자체에 대한 신뢰성을 반영하는 신뢰도 자질을 제안한다. 제안하는 자질은 문서의 내용으로부터 직접 추출되며, 따라서 추천 수나 조회 수 등 서비스 사용자의 참여를 간접적으로 필요로 하는 비텍스트 자질보다 자료 부족 문제에 견고하다는 장점이 있다. 또한 제안하는 신뢰도 자질은 문서 품질 평가에 유용하다고 알려진 비텍스트 자질과 유사하거나 향상된 성능을 실제 지식 질의응답 문서를 대상으로 한 실험에서 보였으며, 추후 효과적인 품질 평가 자질로서 지식 질의응답 서비스의 성능향상에 기여를 할 수 있을 것으로 기대된다.

• 한국과학교육학회지
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• 제32권8호
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• pp.1333-1344
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• 2012

This study aimed to enhance creative problem solving skill by using the Creative Problem Solving (CPS) learning model which was developed based on creative problem solving approach and five essential features of inquiry. The key strategy of the CPS learning model is using real life problem situations to provide students opportunities to practice creative problem solving skill through 5 learning steps: engaging, problem exploring, solutions creating, plan executing, and concepts examining. The science content used for examining the CPS learning model was "matter and properties of matter" that consists of 3 learning units: Matter, Solution, and Acid-Base Solution. The process to assess the effectiveness of the learning model used the experimental design of the Pretest-Posttest Control-Group Design. Seventh grade-students in the experimental group learned by the CPS learning model. At the same time, students at the same grade level in the control group learned by conventional learning model. The learning models and students' prior knowledge levels were served as the independent variables. The creative problem solving skill was classified in to 4 aspects in: fluency, flexibility, originality, and reasoning. The results indicated that in all aspects, the students' mean scores of creative problem solving between students in experimental group and control group were significantly different at the .05 level. Also, the progression of students' creative problem solving skills was found highly progressed at the later instructional periods. When comparing the creative problem solving scores between groups of students with different levels of prior knowledge, the differences of their creative problem solving scores were founded at .05 level. The findings of this study confirmed that the CPS learning model is effective in enhancing the students' creative problem solving skill.

• 대한수학교육학회지:학교수학
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• 제8권2호
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• pp.215-237
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• 2006

본 연구는 국가수준 학업성취도에 따라 구분된 학생들의 성취수준별로 기하 문제해결에서 어떤 특정을 보이는지를 탐색하려 하였다. 기초학력, 보통학력, 우수학력 학생 3 명씩을 동질그룹으로 구성하여 교사의 도움 없이 비정형적인 기하 문제를 해결하게 하였고, 관찰을 통해 성취수준별로 기하 개념 발달 수준이 어떠한지, 문제 해결의 방법을 선택할 때 어떤 접근을 하는지를 분석하였다. 기초학력 학생들은 모양과 실용 기하의 개념 수준에서 문제해결에서 무엇을 할 수 있는가에 초점을 둔 물리적, 구체적 행동을 보였고, 보통학력 학생들은 실용 기하와 유클리드 기하의 수준에서 문제해결을 위해 무엇을 해야 하는가에 초점을 두어 문제해결의 여러 가지 방법을 탐색했으며, 우수학력 학생들은 실용 기하와 유클리드 기하의 수준에서 일반화와 정당화를 통해 문제해결의 본질에 접근하려 하였다. 본 연구는 이에 따라 학생들의 수준별 수학 학습을 지도하는 것에 대한 시사점을 제안하였다.

• 한국멀티미디어학회논문지
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• 제19권2호
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• pp.138-145
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• 2016

LLAH(Locally Likely Arrangement Hashing) is a method which describes image features by exploiting the geometric relationship between their neighbors. Inherently, it is more robust to large view change and poor scene texture than conventional texture-based feature description methods. However, LLAH strongly requires that image features should be detected with high repeatability. The problem is that such requirement is difficult to satisfy in real applications. To alleviate the problem, this paper proposes a method that improves the matching rate of LLAH by exploiting together the brightness of features. Then, it is verified that the matching rate is increased by about 5% in experiments with synthetic images in the presence of Gaussian noise.

• 대한음성학회지:말소리
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• 제65호
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• pp.125-141
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• 2008

In this work, we propose an automatic word spacing system for Korean using conditional random fields (CRFs) with Korean features. We map a word spacing problem into a classification problem in our work. We build a basic system which uses CRFs and Eumjeol bigram. After then, we analyze the result of inner-test. We extend a basic system added by some Korean features which are Josa, Eomi and two head Eumjeols of word extracting from lexicon. From the results of experiment, we can see that the proposed method is better than previous methods. Additionally the proposed method will be able to use mobile and speech applications because of very small size of model.

• Journal of Computing Science and Engineering
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• 제10권4호
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• pp.103-110
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• 2016

Emotion recognition is an important component of affective computing, and is significant in the implementation of natural and friendly human-computer interaction. An effective approach to recognizing emotion from text is based on a machine learning technique, which deals with emotion recognition as a classification problem. However, in emotion recognition, the texts involved are usually very short, leaving a very large, sparse feature space, which decreases the performance of emotion classification. This paper proposes to resolve the problem of feature sparseness, and largely improve the emotion recognition performance from short texts by doing the following: representing short texts with word cluster features, offering a novel word clustering algorithm, and using a new feature weighting scheme. Emotion classification experiments were performed with different features and weighting schemes on a publicly available dataset. The experimental results suggest that the word cluster features and the proposed weighting scheme can partly resolve problems with feature sparseness and emotion recognition performance.