• Title, Summary, Keyword: Automatic Categorization Algorithm

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An Analytical Study on Performance Factors of Automatic Classification based on Machine Learning (기계학습에 기초한 자동분류의 성능 요소에 관한 연구)

  • Kim, Pan Jun
    • Journal of the Korean Society for information Management
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    • v.33 no.2
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    • pp.33-59
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    • 2016
  • This study examined the factors affecting the performance of automatic classification for the domestic conference papers based on machine learning techniques. In particular, In view of the classification performance that assigning automatically the class labels to the papers in Proceedings of the Conference of Korean Society for Information Management using Rocchio algorithm, I investigated the characteristics of the key factors (classifier formation methods, training set size, weighting schemes, label assigning methods) through the diversified experiments. Consequently, It is more effective that apply proper parameters (${\beta}$, ${\lambda}$) and training set size (more than 5 years) according to the classification environments and properties of the document set. and If the performance is equivalent, I discovered that the use of the more simple methods (single weighting schemes) is very efficient. Also, because the classification of domestic papers is corresponding with multi-label classification which assigning more than one label to an article, it is necessary to develop the optimum classification model based on the characteristics of the key factors in consideration of this environment.

Bayesian Automatic Document Categorization Using Apriori-Genetic Algorithm (Apriori-Genetic 알고리즘을 이용한 베이지안 자동 문서 분류)

  • Go, Su-Jeong;Lee, Jeong-Hyeon
    • The KIPS Transactions:PartB
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    • v.8B no.3
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    • pp.251-260
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    • 2001
  • 기존의 베이지안 문서 분류는 문서의 특징 표현에 있어서 단어간의 의미를 정확하게 반영하지 못하는 문제점이 있다. 이러한 문제점을 해결하기 위해, 본 논문에서는 Apriori-Genetic 알고리즘을 이용한 베이지안 문서 분류 방법을 제안한다. Apriori 알고리즘은 단어간의 의미를 반영한 연관 단어의 형태로 문서의 특징을 추출하며 추출된 연관 단어로 연관 단어 지식베이스를 구축한다. Aprrori 알고리즘만으로 연관 단어 지식베이스를 구축할 경우, 지식베이스 안에 부적당한 연관 단어가 포함된다. 따라서 문서 분류의 정확도가 낮아지는 단점이 있다. 이러한 단점을 보완하기 위해, Genetic 알고리즘을 이용하여 연관 단어 지식베이스를 최적화하는 방법을 사용한다. 베이지안 확률을 이용하는 분류자는 최적화된 연관 단어 지식베이스를 기반으로 문서를 클래스별로 분류한다. Apriori-Genetic 알고리즘을 이용한 베이지안 문서 분류의 성능을 평가하기 위해, Apriori 알고리즘을 이용한 베이지안 문서 분류 방법, 역문헌빈도를 사용한 베이지안 문서 분류 방법, 기존의 단순 베이지안 분류 방법과 비교하였다.

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An Analytical Study on Automatic Classification of Domestic Journal articles Based on Machine Learning (기계학습에 기초한 국내 학술지 논문의 자동분류에 관한 연구)

  • Kim, Pan Jun
    • Journal of the Korean Society for information Management
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    • v.35 no.2
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    • pp.37-62
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    • 2018
  • This study examined the factors affecting the performance of automatic classification based on machine learning for domestic journal articles in the field of LIS. In particular, In view of the classification performance that assigning automatically the class labels to the articles in "Journal of the Korean Society for Information Management", I investigated the characteristics of the key factors(weighting schemes, training set size, classification algorithms, label assigning methods) through the diversified experiments. Consequently, It is effective to apply each element appropriately according to the classification environment and the characteristics of the document set, and a fairly good performance can be obtained by using a simpler model. In addition, the classification of domestic journals can be considered as a multi-label classification that assigns more than one category to a specific article. Therefore, I proposed an optimal classification model using simple and fast classification algorithm and small learning set considering this environment.

Weighted Bayesian Automatic Document Categorization Based on Association Word Knowledge Base by Apriori Algorithm (Apriori알고리즘에 의한 연관 단어 지식 베이스에 기반한 가중치가 부여된 베이지만 자동 문서 분류)

  • 고수정;이정현
    • Journal of Korea Multimedia Society
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    • v.4 no.2
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    • pp.171-181
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    • 2001
  • The previous Bayesian document categorization method has problems that it requires a lot of time and effort in word clustering and it hardly reflects the semantic information between words. In this paper, we propose a weighted Bayesian document categorizing method based on association word knowledge base acquired by mining technique. The proposed method constructs weighted association word knowledge base using documents in training set. Then, classifier using Bayesian probability categorizes documents based on the constructed association word knowledge base. In order to evaluate performance of the proposed method, we compare our experimental results with those of weighted Bayesian document categorizing method using vocabulary dictionary by mutual information, weighted Bayesian document categorizing method, and simple Bayesian document categorizing method. The experimental result shows that weighted Bayesian categorizing method using association word knowledge base has improved performance 0.87% and 2.77% and 5.09% over weighted Bayesian categorizing method using vocabulary dictionary by mutual information and weighted Bayesian method and simple Bayesian method, respectively.

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A Study on Book Categorization in Social Sciences Using kNN Classifiers and Table of Contents Text (목차 정보와 kNN 분류기를 이용한 사회과학 분야 도서 자동 분류에 관한 연구)

  • Lee, Yong-Gu
    • Journal of the Korean Society for information Management
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    • v.37 no.1
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    • pp.1-21
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    • 2020
  • This study applied automatic classification using table of contents (TOC) text for 6,253 social science books from a newly arrived list collected by a university library. The k-nearest neighbors (kNN) algorithm was used as a classifier, and the ten divisions on the second level of the DDC's main class 300 given to books by the library were used as classes (labels). The features used in this study were keywords extracted from titles and TOCs of the books. The TOCs were obtained through the OpenAPI from an Internet bookstore. As a result, it was found that the TOC features were good for improving both classification recall and precision. The TOC was shown to reduce the overfitting problem of imbalanced data with its rich features. Law and education have high topic specificity in the field of social sciences, so the only title features can bring good classification performance in these fields.

Automatic Text Categorization Using Hybrid Multiple Model Schemes (하이브리드 다중모델 학습기법을 이용한 자동 문서 분류)

  • 명순희;김인철
    • Journal of the Korean Society for information Management
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    • v.19 no.4
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    • pp.35-51
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    • 2002
  • Inductive learning and classification techniques have been employed in various research and applications that organize textual data to solve the problem of information access. In this study, we develop hybrid model combination methods which incorporate the concepts and techniques for multiple modeling algorithms to improve the accuracy of text classification, and conduct experiments to evaluate the performances of proposed schemes. Boosted stacking, one of the extended stacking schemes proposed in this study yields higher accuracy relative to the conventional model combination methods and single classifiers.

A Study on the Performance Improvement of Rocchio Classifier with Term Weighting Methods (용어 가중치부여 기법을 이용한 로치오 분류기의 성능 향상에 관한 연구)

  • Kim, Pan-Jun
    • Journal of the Korean Society for information Management
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    • v.25 no.1
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    • pp.211-233
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    • 2008
  • This study examines various weighting methods for improving the performance of automatic classification based on Rocchio algorithm on two collections(LISA, Reuters-21578). First, three factors for weighting are identified as document factor, document factor, category factor for each weighting schemes, the performance of each was investigated. Second, the performance of combined weighting methods between the single schemes were examined. As a result, for the single schemes based on each factor, category-factor-based schemes showed the best performance, document set-factor-based schemes the second, and document-factor-based schemes the worst. For the combined weighting schemes, the schemes(idf*cat) which combine document set factor with category factor show better performance than the combined schemes(tf*cat or ltf*cat) which combine document factor with category factor as well as the common schemes (tfidf or ltfidf) that combining document factor with document set factor. However, according to the results of comparing the single weighting schemes with combined weighting schemes in the view of the collections, while category-factor-based schemes(cat only) perform best on LISA, the combined schemes(idf*cat) which combine document set factor with category factor showed best performance on the Reuters-21578. Therefore for the practical application of the weighting methods, it needs careful consideration of the categories in a collection for automatic classification.

Automatic Interface Synthesis based on IP Categorization and Characteristics Matching (IP 범주화와 특성 대응을 통한 인터페이스 회로 자동 합성)

  • Yun, Chang-Ryul;Jhang, Kyoung-Son
    • Journal of the Institute of Electronics Engineers of Korea SD
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    • v.43 no.10
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    • pp.34-44
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    • 2006
  • A system-on-a-chip (SoC) design uses pre-verified IP hardware blocks in order to reduce design time. We need interface circuits to connect IPs with different protocols. In SoC design we should design interface circuits frequently and these tasks are somewhat time-consuming and error-prone. So it is necessary to generate the interface circuits automatically. Several studies have been made on generating interface circuits only from the communication protocols of IPs. With existing approaches, it is not easy to generate interface circuits connecting two IPs only from communication protocols: connection between IP with address and W without address, connection between IP with only one port to transfer address/data and IP with different ports for address and data connection between IP that transfer address and data together and IP that transfer only one address with a number of data in a burst. No consideration of various characteristics of IPs and no changed algorithm are responsible for it. In order to solve this problem, the proposed approach categorizes communication protocols of IPs, and takes characteristics matching of IPs into account during the interface synthesis. In experiments, we show that we could correctly generate and verify interface circuits for IPs with different characteristics.

Classifying Windows Executables using API-based Information and Machine Learning (API 정보와 기계학습을 통한 윈도우 실행파일 분류)

  • Cho, DaeHee;Lim, Kyeonghwan;Cho, Seong-je;Han, Sangchul;Hwang, Young-sup
    • Journal of KIISE
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    • v.43 no.12
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    • pp.1325-1333
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    • 2016
  • Software classification has several applications such as copyright infringement detection, malware classification, and software automatic categorization in software repositories. It can be also employed by software filtering systems to prevent the transmission of illegal software. If illegal software is identified by measuring software similarity in software filtering systems, the average number of comparisons can be reduced by shrinking the search space. In this study, we focused on the classification of Windows executables using API call information and machine learning. We evaluated the classification performance of machine learning-based classifier according to the refinement method for API information and machine learning algorithm. The results showed that the classification success rate of SVM (Support Vector Machine) with PolyKernel was higher than other algorithms. Since the API call information can be extracted from binary executables and machine learning-based classifier can identify tampered executables, API call information and machine learning-based software classifiers are suitable for software filtering systems.