• Title, Summary, Keyword: Multi-label 분류

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Improving Accuracy of Multi-label Naive Bayes Classifier (다중 레이블 나이브 베이지안 분류기의 정확도 개선 연구)

  • Kim, Hae-Choen;Lee, Jae-Sung
    • Proceedings of the Korean Society of Computer Information Conference
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    • pp.147-148
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    • 2018
  • 다중 레이블 분류 문제는 다중 레이블 데이터를 입력받았을 때 연관된 다수의 레이블을 추측하는 문제이다. 본 논문에서는 다중 레이블 분류 문제의 기법 중 하나인 나이브 베이지안 분류기에 레이블 의존성을 계산하여 결과에 반영한 결과 다중 레이블 분류 문제의 성능이 개선됨을 확인하였다.

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A Performance Comparison of Multi-Label Classification Methods for Protein Subcellular Localization Prediction (단백질의 세포내 위치 예측을 위한 다중레이블 분류 방법의 성능 비교)

  • Chi, Sang-Mun
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.18 no.4
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    • pp.992-999
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    • 2014
  • This paper presents an extensive experimental comparison of a variety of multi-label learning methods for the accurate prediction of subcellular localization of proteins which simultaneously exist at multiple subcellular locations. We compared several methods from three categories of multi-label classification algorithms: algorithm adaptation, problem transformation, and meta learning. Experimental results are analyzed using 12 multi-label evaluation measures to assess the behavior of the methods from a variety of view-points. We also use a new summarization measure to find the best performing method. Experimental results show that the best performing methods are power-set method pruning a infrequently occurring subsets of labels and classifier chains modeling relevant labels with an additional feature. futhermore, ensembles of many classifiers of these methods enhance the performance further. The recommendation from this study is that the correlation of subcellular locations is an effective clue for classification, this is because the subcellular locations of proteins performing certain biological function are not independent but correlated.

Multi-Label Combination for Prediction of Protein Subcellular Localization (다중레이블 조합을 사용한 단백질 세포내 위치 예측)

  • Chi, Sang-Mun
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.18 no.7
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    • pp.1749-1756
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    • 2014
  • Knowledge about protein subcellular localization provides important information about protein function. This paper improves a label power-set multi-label classification for the accurate prediction of subcellular localization of proteins which simultaneously exist at multiple subcellular locations. Among multi-label classification methods, label power-set method can effectively model the correlation between subcellular locations of proteins performing certain biological function. With constrained optimization, this paper calculates combination weights which are used in the linear combination representation of a multi-label by other multi-labels. Using these weights, the prediction probabilities of multi-labels are combined to give final prediction results. Experimental results on human protein dataset show that the proposed method achieves higher performance than other prediction methods for protein subcellular localization. This shows that the proposed method can successfully enrich the prediction probability of multi-labels by exploiting the overlapping information between multi-labels.

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.

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.

Prediction of Protein Subcellular Localization using Label Power-set Classification and Multi-class Probability Estimates (레이블 멱집합 분류와 다중클래스 확률추정을 사용한 단백질 세포내 위치 예측)

  • Chi, Sang-Mun
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.18 no.10
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    • pp.2562-2570
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    • 2014
  • One of the important hints for inferring the function of unknown proteins is the knowledge about protein subcellular localization. Recently, there are considerable researches on the prediction of subcellular localization of proteins which simultaneously exist at multiple subcellular localization. In this paper, label power-set classification is improved for the accurate prediction of multiple subcellular localization. The predicted multi-labels from the label power-set classifier are combined with their prediction probability to give the final result. To find the accurate probability estimates of multi-classes, this paper employs pair-wise comparison and error-correcting output codes frameworks. Prediction experiments on protein subcellular localization show significant performance improvement.

IPC Multi-label Classification based on Functional Characteristics of Fields in Patent Documents (특허문서 필드의 기능적 특성을 활용한 IPC 다중 레이블 분류)

  • Lim, Sora;Kwon, YongJin
    • Journal of Internet Computing and Services
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    • v.18 no.1
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    • pp.77-88
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    • 2017
  • Recently, with the advent of knowledge based society where information and knowledge make values, patents which are the representative form of intellectual property have become important, and the number of the patents follows growing trends. Thus, it needs to classify the patents depending on the technological topic of the invention appropriately in order to use a vast amount of the patent information effectively. IPC (International Patent Classification) is widely used for this situation. Researches about IPC automatic classification have been studied using data mining and machine learning algorithms to improve current IPC classification task which categorizes patent documents by hand. However, most of the previous researches have focused on applying various existing machine learning methods to the patent documents rather than considering on the characteristics of the data or the structure of patent documents. In this paper, therefore, we propose to use two structural fields, technical field and background, considered as having impacts on the patent classification, where the two field are selected by applying of the characteristics of patent documents and the role of the structural fields. We also construct multi-label classification model to reflect what a patent document could have multiple IPCs. Furthermore, we propose a method to classify patent documents at the IPC subclass level comprised of 630 categories so that we investigate the possibility of applying the IPC multi-label classification model into the real field. The effect of structural fields of patent documents are examined using 564,793 registered patents in Korea, and 87.2% precision is obtained in the case of using title, abstract, claims, technical field and background. From this sequence, we verify that the technical field and background have an important role in improving the precision of IPC multi-label classification in IPC subclass level.

Multi-labeled Domain Detection Using CNN (CNN을 이용한 발화 주제 다중 분류)

  • Choi, Kyoungho;Kim, Kyungduk;Kim, Yonghe;Kang, Inho
    • 한국어정보학회:학술대회논문집
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    • pp.56-59
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    • 2017
  • CNN(Convolutional Neural Network)을 이용하여 발화 주제 다중 분류 task를 multi-labeling 방법과, cluster 방법을 이용하여 수행하고, 각 방법론에 MSE(Mean Square Error), softmax cross-entropy, sigmoid cross-entropy를 적용하여 성능을 평가하였다. Network는 음절 단위로 tokenize하고, 품사정보를 각 token의 추가한 sequence와, Naver DB를 통하여 얻은 named entity 정보를 입력으로 사용한다. 실험결과 cluster 방법으로 문제를 변형하고, sigmoid를 output layer의 activation function으로 사용하고 cross entropy cost function을 이용하여 network를 학습시켰을 때 F1 0.9873으로 가장 좋은 성능을 보였다.

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Automatic Email Multi-category Classification Using Dynamic Category Hierarchy and Non-negative Matrix Factorization (비음수 행렬 분해와 동적 분류 체계를 사용한 자동 이메일 다원 분류)

  • Park, Sun;An, Dong-Un
    • Journal of KIISE:Software and Applications
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    • v.37 no.5
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    • pp.378-385
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    • 2010
  • The explosive increase in the use of email has made to need email classification efficiently and accurately. Current work on the email classification method have mainly been focused on a binary classification that filters out spam-mails. This methods are based on Support Vector Machines, Bayesian classifiers, rule-based classifiers. Such supervised methods, in the sense that the user is required to manually describe the rules and keyword list that is used to recognize the relevant email. Other unsupervised method using clustering techniques for the multi-category classification is created a category labels from a set of incoming messages. In this paper, we propose a new automatic email multi-category classification method using NMF for automatic category label construction method and dynamic category hierarchy method for the reorganization of email messages in the category labels. The proposed method in this paper, a large number of emails are managed efficiently by classifying multi-category email automatically, email messages in their category are reorganized for enhancing accuracy whenever users want to classify all their email messages.

Multi-labeled Domain Detection Using CNN (CNN을 이용한 발화 주제 다중 분류)

  • Choi, Kyoungho;Kim, Kyungduk;Kim, Yonghe;Kang, Inho
    • Annual Conference on Human and Language Technology
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    • pp.56-59
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    • 2017
  • CNN(Convolutional Neural Network)을 이용하여 발화 주제 다중 분류 task를 multi-labeling 방법과, cluster 방법을 이용하여 수행하고, 각 방법론에 MSE(Mean Square Error), softmax cross-entropy, sigmoid cross-entropy를 적용하여 성능을 평가하였다. Network는 음절 단위로 tokenize하고, 품사정보를 각 token의 추가한 sequence와, Naver DB를 통하여 얻은 named entity 정보를 입력으로 사용한다. 실험결과 cluster 방법으로 문제를 변형하고, sigmoid를 output layer의 activation function으로 사용하고 cross entropy cost function을 이용하여 network를 학습시켰을 때 F1 0.9873으로 가장 좋은 성능을 보였다.

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