• Korean Journal of Cognitive Science
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• v.16 no.1
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• pp.53-67
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• 2005

Present study identified categorization pattern differences between experts and novices and examined whether categorization training has positive effects on problem solving. In experiment I, we examined categorization differences between groups according to expertise using mathematical equation problems. Experts classified problems based on deep structure related to problem solution methods whereas novices classified problems based on surface features. However, in the labeled categorization condition, novices' categorization pattern was not different from experts'. These results suggest that novices have difficulty identifying deep structure of problems. In experiment 2, we examined whether categorization training showing subjects deep structure of problems explicitly increases transfer performance. The results showed that solution training was more effective to expert group whereas categorization training was more effective to novice group. We have discussed that different training methods should be applied according to expertise.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.364-366
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• 2001

문서 범주화는 전자 정보환경에서 매우 유용한 정보처리 도구로서, 다양한 문서 범주화 기법 및 성능향상을 위한 연구들이 지속적으로 이루어지고 있다. 그러나, 대부분의 연구들은 문서 범주화의 대상이 되는 단어 자질 공간의 차원축소 문제에만 집중되었을 뿐, 학습단계에 큰 영향을 미치는 다범주 단어 자질의 범주 모호성은 고려하지 않았다. 본 연구에서는, 다범주 자질의 범주 모호성을 해소함으로써 문서 범주화의 성능향상을 유도하는 범주 모호성 해소 가중치 W를 제시하고 이를 실험을 통해 증명하였다. 실험에서는 Naive Bayes 분류기와 가중치 W를 적용한 Naive Bayes-W 분류기를 직접 구축하여 문서 범주화의 성능향상 여부를 비교하는데 사용하였다. 도출된 실험결과를 통해, 가중치 W는 현재의 분류기가 가지고 있는 자질 표현의 범주 모호성이라는 단점을 보완하고 분류기의 성능향상을 유도함으로써 정보검색시스템의 검색효율을 높이는 데 활용될 수 있음일 증명되었다.

• Journal of KIISE:Software and Applications
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• v.31 no.7
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• pp.849-858
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• 2004

Most of supervised teaming algorithms could be applied after that continuous variables are transformed to categorical ones at the preprocessing stage in order to avoid the difficulty of processing continuous variables. This preprocessing stage is called global discretization, uses the class distribution list called bins. But, when data are large and the range of the variable to be discretized is very large, many sorting and merging should be performed to produce a single bin because most of global discretization methods need a single bin. Also, if new data are added, they have to perform discretization from scratch to construct categories influenced by the data because the existing methods perform discretization in batch mode. This paper proposes a method that extracts sample points and performs discretization from these sample points in order to solve these problems. Because the approach in this paper does not require merging for producing a single bin, it is efficient when large data are needed to be discretized. In this study, an experiment using real and synthetic datasets was made to compare the proposed method with an existing one.

• Journal of KIISE:Software and Applications
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• v.35 no.5
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• pp.325-334
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• 2008

The goal of text categorization is to classify documents into a certain number of pre-defined categories. The previous studies in this area have used a large number of labeled training documents for supervised learning. One problem is that it is difficult to create the labeled training documents. While it is easy to collect the unlabeled documents, it is not so easy to manually categorize them for creating training documents. In this paper, we propose a new text categorization method based on semi-supervised learning. The proposed method uses only unlabeled documents and keywords of each category, and it automatically constructs training data from them. Then a text classifier learns with them and classifies text documents. The proposed method shows a similar degree of performance, compared with the traditional supervised teaming methods. Therefore, this method can be used in the areas where low-cost text categorization is needed. It can also be used for creating labeled training documents.

• 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.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.363-365
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• 1998

텍스트 마이닝(Text Mining)이란 텍스트형태의 문서들의 패턴 또는 관계를 추출하여 사용자가 원하는 새로운 정보를 가공하거나 기존의 정보를 변형하는 과정을 말한다. 텍스트 마이닝의 기능에는 문서 범주화(Document Categorization), 문서 군집화(Document Clustering), 그리고 문서 요약(Document Summarization)이 이에 해당된다. 문서 범주화란 문서에게 사전에 정의한 범주를 부여하는 과정을 말하고, 문서 군집화란 문서들을 계층적 구조로 형성하는 과정을 말하고, 문서 요약이란 문서의 전체 내용을 대표할 수 있는 내용의 일부만을 추출하는 과정을 말한다. 이 논문에서는 문서 범주화만을 다룰 것이며 그 대상으로는 신문기사로 설정하였다. 그의 범주는 4가지로 정치, 경제, 스포츠, 그리고 정보통신으로 설정하였다. 문서 범주화는 문서 분류(Document Classification)라고도 하며 문서에 범주를 자동으로 부여하여 기존에 인위적으로 부여함으로써 소요되는 시간과 비용을 절감하는 것이 목적이다. 문서 범주화에 대하여 k-NN(k-Nearest Neighbor)와 신경망을 이용하였으며, 신경망을 이용한 경우가 k-NN을 이용한 경우보다 성능이 우수하였다.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.1150-1155
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• 2009

In the information society, the amount of information have been increased by technological development. It is not easy to deal with information for fast data processing because of increasing of the complexity and diversity of data. So this paper will confirm the fact that the color plays the role of the classification of complex information and can make data processing fast. Experiment 1 shows that the searching time of target(line name) is more faster when the color of a subway line is equal to the color of station`s name. Experiment 2 using the task for classification of word mixed in various categories shows that color category processing is more faster rather than semantic category processing and the effect of this task is far better when color difference is more clear.

• Journal of the Korea Society of Computer and Information
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• v.19 no.3
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• pp.17-24
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• 2014

In this paper, we propose a mobile application categorization method using word cluster information. Because the mobile application description can be shortly written, the proposed method utilizes the word cluster seeds as well as the words in the mobile application description, as categorization features. For the fragmented categories of the mobile applications, the proposed method generates the word clusters by applying the frequency of word occurrence per category to K-means clustering algorithm. Since the mobile application description can include some paragraphs unrelated to the categorization, such as installation specifications, the proposed method uses some word clusters useful for the categorization. Experiments show that the proposed method improves the recall (5.65%) by using the word cluster information.

• Proceedings of the Korean Society for Information Management Conference
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• 2000.08a
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• pp.147-152
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• 2000

본 연구는 다양한 웹 문서를 효과적으로 범주화 할 수 있는 모형을 구축하는데 그 목적이 있다. 이를 위해 본 연구에서는 웹 문서가 가지고 있는 구조 정보인 링크(link)와 문서 단계(level)를 활용하여 문서 유형을 식별한 후, 각 유형별로 범주화 과정을 달리 적용하여 범주화 성능을 개선시키는 방법을 고안하였다.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.487-498
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• 2005

Recently, It has focused on decision tree algorithm that can handle large dataset. However, because most of these algorithms for large datasets process data in a batch mode, if new data is added, they have to rebuild the tree from scratch. h more efficient approach to reducing the cost problem of rebuilding is an approach that builds a tree incrementally. Representative algorithms for incremental tree construction methods are BOAT and ITI and most of these algorithms use a local discretization method to handle the numeric data type. However, because a discretization requires sorted numeric data in situation of processing large data sets, a global discretization method that sorts all data only once is more suitable than a local discretization method that sorts in every node. This paper proposes an incremental tree construction method that efficiently rebuilds a tree using a global discretization method to handle the numeric data type. When new data is added, new categories influenced by the data should be recreated, and then the tree structure should be changed in accordance with category changes. This paper proposes a method that extracts sample points and performs discretiration from these sample points to recreate categories efficiently and uses confidence intervals and a tree restructuring method to adjust tree structure to category changes. In this study, an experiment using people database was made to compare the proposed method with the existing one that uses a local discretization.