• Journal of the Korean School Mathematics Society
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• v.14 no.3
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• pp.277-293
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• 2011

The purpose of mathematics education is to develop the ability of transforming various problems in general situations into mathematics problems and then solving the problem mathematically. Various teaching-learning methods for improving the ability of the mathematics problem-solving can be tried. However, it is necessary to choose an appropriate teaching-learning method after figuring out students' level of understanding the mathematics learning or their problem-solving strategies. The error analysis is helpful for mathematics learning by providing teachers more efficient teaching strategies and by letting students know the cause of failure and then find a correct way. The following subjects were set up and analyzed. First, the error classification pattern was set up. Second, the errors in the solving process of the function problems were analyzed according to the error classification pattern. For this study, the survey was conducted to 90 first grade students of ${\bigcirc}{\bigcirc}$high school in Chung-nam. They were asked to solve 8 problems in the function part. The following error classification patterns were set up by referring to the preceding studies about the error and the error patterns shown in the survey. (1)Misused Data, (2)Misinterpreted Language, (3)Logically Invalid Inference, (4)Distorted Theorem or Definition, (5)Unverified Solution, (6)Technical Errors, (7)Discontinuance of solving process The results of the analysis of errors due to the above error classification pattern were given below First, students don't understand the concept of the function completely. Even if they do, they lack in the application ability. Second, students make many mistakes when they interpret the mathematics problem into different types of languages such as equations, signals, graphs, and figures. Third, students misuse or ignore the data given in the problem. Fourth, students often give up or never try the solving process. The research on the error analysis should be done further because it provides the useful information for the teaching-learning process.

• The Journal of the Korea Contents Association
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• v.17 no.9
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• pp.650-660
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• 2017

This study is to suggest a new method of analyzing personality types of characters in narrative. First, we examined the history of the taxonomy of character types that existed in narrative theories so far. Until now, the classification of character types in narrative theory consisted largely of a formal classification based on roles in narrative, a content classification based on human internal qualities, and a complementary classification in which the two classification criteria are united. The problem with the existing character classification type is difficult to categorize it in spite of the usefulness of the content classification based on human internal qualities. On the other hand, the classification based on the role of the character in the narrative does not help as much as a practical analysis methodology because the classification is formal. In this study, we try to solve this problem by introducing Shalom Schwartz's human value type, and to make human character's value type and human role correlated with each other as a new character analysis methodology. Schwartz's study of value type is a very effective method to grasp the motivation of human behavior, and it seems to be very meaningful in analyzing the directivity of characters.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.360-366
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• 2008

The initial fuzzy partitions in fuzzy rule-based classification systems are determined by considering the domain region of each attribute with the given data, and the optimal classification boundaries within the fuzzy partitions can be discovered by tuning their parameters using various learning processes such as neural network, genetic algorithm, and so on. In this paper, we propose a selection method for fuzzy partition based on statistical information to maximize the performance of pattern classification without learning processes where statistical information is used to extract the uncertainty regions (i.e., the regions which the classification boundaries in pattern classification problems are determined) in each input attribute from the numerical data. Moreover the methods for extracting the candidate rules which are associated with the partition intervals generated by statistical information and for minimizing the coupling problem between the candidate rules are additionally discussed. In order to show the effectiveness of the proposed method, we compared the classification accuracy of the proposed with those of conventional methods on the IRIS and New Thyroid Cancer data. From experimental results, we can confirm the fact that the proposed method only considering statistical information of the numerical patterns provides equal to or better classification accuracy than that of the conventional methods.

• Journal of Korean Library and Information Science Society
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• v.49 no.4
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• pp.265-294
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• 2018

Nowadays, due to an explosive increase in information, information filtering is very important to provide proper information for users. Users hardly obtain scholarly information from a huge amount of information in NDSL of KISTI, except for simple search. In this paper, we propose the service, PIN to solve this problem. Pin provides the word cloud including analyzed users' and others' interesting, co-occurence, and searched keywords, rather than the existing word cloud simply consisting of all keywords and so offers user-customized papers, reports, patents, and trends. In addition, PIN gives the paper classification in NDSL according to keyword matching based classification with the overlapping classification enabled-academic classification system for better search and access to solve this problem. In this paper, Keywords are extracted according to the classification from papers published in Korean journals in 2016 to design classification model and we verify this model.

• Journal of Internet Computing and Services
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• v.1 no.1
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• pp.63-72
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• 2000

Data mining means a process of nontrivial extraction of hidden knowledge or potentially useful information from data in large databases. Data mining algorithm is a multi-disciplinary field of research; machine learning, statistics, and computer science all make a contribution. Different classification schemes can be used to categorize data mining methods based on the kinds of tasks to be implemented and the kinds of application classes to be utilized, and classification has been identified as an important task in the emerging field of data mining. Since classification is the basic element of human's way of thinking, it is a well-studied problem in a wide varietyof application. In this paper, we propose a classifier system based on genetic algorithm with robust property, and the proposed system is evaluated by applying it to nDmC problem related to classification task in data mining.

• Journal of KIISE:Software and Applications
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• v.32 no.9
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• pp.912-919
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• 2005

When we apply binary classification to multi-class classification for text categorization, we use the One-Against-All method generally, However, this One-Against-All method has a problem. That is, documents of a negative set are not labeled by human. Thus, they can include many noisy documents in the training data. In this paper, we propose that the Sliding Window technique and the EM algorithm are applied to binary text classification for solving this problem. We here improve binary text classification through extracting noise documents from the training data by the Sliding Window technique and re-assigning categories of these documents using the EM algorithm.

• International Journal of Contents
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• v.10 no.1
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• pp.43-46
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• 2014

Subject classification of journals is important because it can be utilized for the improvement of scholarly information services and analysis by research area. The classification by experts in a subject area wastes a lot of time and expense. On the other hand, the simple classification with basic information, such as the journal title has limitations. To solve this problem, this paper suggests the automatic classification of Korean journals using the SCI journals information cited by Korean journals, and an analysis of the classification result. In particular, this study adopted the WoS subject categories for classification to support the base for comparison between the Korean citation database and the global citation database (KSCI vs. SCI).

• Journal of Korean Medical classics
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• v.36 no.3
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• pp.89-128
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• 2023

Objectives : This study aims to diachronically examine the classification systems of all materia medica, followed by categorization and analysis of each category to deduce each category's characteristic. This will provide foundation for further examining classifications of bencao in contemporary herbology. Methods : Classification systems from a total of 93 bencao related texts were collected and categorized. Each category's classification system was analyzed to determine its meaning. The classification systems were compared from a diachronic perspective, to further deduce each system's problem from a historical context. Results : The classification systems of materia medica could be summarized as following three standards: quality, origin, and medical application. In reality, bencao could be generally classified according to origin and medical application. The origin-based classification system provided a stable and flexible classification outline in the expansion process of bencao. The medical application-based classification strengthened the relationship between bencao and illness pattern, improving clinical applicability. Conclusions : In the history of herbology, the two classification systems created the current of herbology through mutual contribution and conflict. We hope that further discussion on the direction towards which classification system of bencao in contemporary herbology should head will proceed based on this study.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.277-279
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• 2021

There is a problem that learning of a prediction model is not well performed depending on the characteristics of each particular matter concentration. To solve this problem, it is necessary to design a prediction model for low concentration and high concentration separately. Therefore, a classification model is needed to classify the concentration of particular matter into low and high concentrations. This paper proposes a classification model to classify low and high concentrations based on the concentration of particular matter. DNN was used as the classification model algorithm, and the classification model was designed by applying the optimal parameters after searching for hyper parameters. As for the result of evaluating the performance of the model, 97.54% of the low concentration classification was measured. And in the case of high concentration classification, 85.51% was measured.

• Journal of Korea Multimedia Society
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• v.23 no.3
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• pp.447-459
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• 2020

The Internet of Things(IoT) paradigm is flourishing strenuously for the last two decades. Researchers around the globe have their dreams to transmute every real-world object to the virtual object. Consequently, IoT devices are escalating exponentially. The abrupt evolution of these IoT devices has caused a major challenge i.e. object classification. In order to classify devices comprehensively and accurately, this paper proposes a context-aware based multi-classification model for devices, which classifies the smart devices according to people's contexts. However, the classification features of contextual data of different contexts are difficult to extract. The deep learning algorithm has the capability to solve this problem. This paper proposes a context-aware based multi-classification model of devices, which classifies the smart devices according to people's contexts.