• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.175-184
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• 2008

To construct user profiles automatically, an extraction method for representative keywords from a set of documents is needed. In our previous works, we suggested such a method and showed its usefulness. Here, we apply it to the classification problem and observe how much it contributes to performance improvement. The method can be used as a linear document classifier with few modifications. So, we first evaluate its performance for that case. The method is also applicable to some non-linear classification methods such as GIS (Generalized Instance Set). In GIS algorithm, generalized instances are built from training documents by a generalization function and then the K-NN algorithm is applied to them, where the method can be used as a generalization function. For comparative works, two famous linear classification methods, Rocchio and Widrow-Hoff algorithms, are also used. Experimental results show that our method is better than the others for the case that only positive documents are considered, but not when negative documents are considered together.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.301-304
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• 2005

For detecting an intrusion based on the anomaly of a user's activities, previous works are concentrated on statistical techniques or frequent episode mining in order to analyze an audit data. But, since they mainly analyze the average behaviour of user's activities, some anomalies can be detected inaccurately. Therefore, we propose an anomaly detection method that utilizes an associative classification for modelling intrusion detection. Finally, we proof that a prediction model built from associative classification method yields better accuracy than a prediction model built from a traditional methods by experimental results.

• Industrial Engineering and Management Systems
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• v.13 no.4
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• pp.421-431
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• 2014

The market basket data in the form of a binary user-item matrix or a binary item-user matrix can be modelled as a binary classification problem. The binary logistic regression approach tackles the binary classification problem, where principal components are predictor variables. If users or items are sparse in the training data, the binary classification problem can be considered as a cold-start problem. The binary logistic regression approach may not function appropriately if the principal components are inefficient for the cold-start problem. Assuming that the market basket data can also be considered as a special regression problem whose response is either 0 or 1, we propose three supervised learning approaches: random forest regression, random forest classification, and elastic net to tackle the cold-start problem, comparing the performance in a variety of experimental settings. The experimental results show that the proposed supervised learning approaches outperform the conventional approaches.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.657-668
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• 2001

It is difficult that web documents are classified with exact user intention because existing document classification systems are based on word frequency number using single keyword. To improve this defect, first, we use keyword, a query, domain knowledge. Like explanation based learning, first, query is analyzed with knowledge based information and then structured user intention information is extracted. We use this intention tree in the course of existing word frequency number based document classification as user information and constraints. Thus, we can classify web documents with more exact user intention. In classifying document, structured user intention information is helpful to keep more documents and information which can be lost in the system using single keyword information. Our hybrid approach integrating user intention information with existing statistics and probability method is more efficient to decide direction and range of document category than existing word frequency approach.

• Journal of Integrative Natural Science
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• v.11 no.3
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• pp.131-138
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• 2018

In this paper, we have implemented speeches that utilized the emotion information of the user's speech and image matching and recommendation system. To classify the user's emotional information of speech, the emotional information of speech about the user's speech is extracted and classified using the PLP algorithm. After classification, an emotional DB of speech is constructed. Moreover, emotional color and emotional vocabulary through factor analysis are matched to one space in order to classify emotional information of image. And a standardized image recommendation system based on the matching of each keyword with the BM-GA algorithm for the data of the emotional information of speech and emotional information of image according to the more appropriate emotional information of speech of the user. As a result of the performance evaluation, recognition rate of standardized vocabulary in four stages according to speech was 80.48% on average and system user satisfaction was 82.4%. Therefore, it is expected that the classification of images according to the user's speech information will be helpful for the study of emotional exchange between the user and the computer.

• Journal of Korea Game Society
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• v.7 no.4
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• pp.23-31
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• 2007

The study is to explore the ways to design game characters according to the tendency of game users by classifying game users and analyzing the relation between user classes and their preferences towards game characters. The study examines various user classifications based on users' engagement levels, and designs a user questionnaire from them. Based on the result of questionnaire analysis, the study redefines user classes and applies the formal elements of character design to draw on the relationships between user classes and their preferences.

• Journal of Digital Convergence
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• v.17 no.8
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• pp.401-406
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• 2019

Recently, as multi-platform game environments become common, many games of convergence genre have been produced, and the boundaries of genre division by existing platforms have become blurred. The game genre is convergence content consisting of user-centered 'narrative and play'. In this paper, we propose a game genre classification according to the user 's behavior type based on the essential recognition that the subject of the game is the user. The user's actions are done in different genres and goals and rules, and the interaction is an important act for immersion. Therefore, the user's behavioral classification and perception by the game genre are important and expected to help redefine the game structure.

• Proceedings of the Korean Institute of Interior Design Conference
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• 2001.05a
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• pp.121-127
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• 2001

An unified measure of user interface efficiency and aesthetics for cyber museum is proposed. First, general structure of cyber museum is discussed and hierarchical analyses are done for sample sites. Usability tests based on the hierarchical analyses yield statistics of user access frequency and persistency for each page, on which access probability is deduced. Second, visual occupancy, a measure of efficiency of user interface element based on access probability is defined. The hierarchical statistics of visual occupancy can be an index for characterization and classification of cyber museums. Examples are provided.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.31 no.1
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• pp.115-136
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• 2020

It is necessary to find a genre classification by reflecting the needs of users since a subject that makes up the highest proportion of books in the school library is fictions in literature and KDC cannot accept user's need to access fiction in school libraries. This study suggested the genre classification for fictions in school libraries through surveying classification of fictions in domestic and foreign libraries, and comparing between classification systems of online/offline bookstores, KDC and DDC. For developing the genre classification system, it is to collect genre terms for fictions, to extract 14 genre headings among them, and to assign the acronym of English genre terms as classification notation. For applying the newly developed genre classification, KDC number of one middle school library was converted as the 3 methods such as combination of KDC, genre term before 800 and only genre terms. This study could contribute to suggest the genre classification of fiction to reflect user needs and to overcome the limitation of hierachical classification in KDC.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.160-166
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• 2007

When a mail was given to users, each user's response could be different according to his or her preference. This paper presents a solution for this situation by constructing a user preferred ontology for anti-spam systems. To define an ontology for describing user behaviors, we applied associative classification mining to study preference information of users and their responses to emails. Generated classification rules can be represented in a formal ontology language. A user preferred ontology can explain why mail is decided to be spam or ron-spam in a meaningful way. We also suggest a new rule optimization procedure inspired from logic synthesis to improve comprehensibility and exclude redundant rules.