다중 레이블 나이브 베이즈 분류를 위한 새로운 사후확률 추정 방법에 관한 연구

다국어 초록 (Multilingual Abstract)

A multi-label classification is to find multiple labels associated with the input pattern. Multi-label classification can be achieved by extending conventional single-label classification. Common extension techniques are known as Binary relevance, Label powerset, and Classifier chains. However, most of the extended multi-label naive bayes classifier has not been able to accurately estimate posterior probabilities because it does not reflect the label dependency. And the remaining extended multi-label naive bayes classifier has a problem that it is unstable to estimate posterior probability according to the label selection order. To estimate posterior probability well, we propose a new posterior probability estimation method that reflects the probability between all labels and labels efficiently. The proposed method reflects the correlation between labels. And we have confirmed through experiments that the extended multi-label naive bayes classifier using the proposed method has higher accuracy then the existing multi-label naive bayes classifiers.

참고문헌 (Reference)

1 박종범, "인공지능 포커게임수행엔진 개발" 한국컴퓨터정보학회 14 (14): 41-52, 2009

2 백영태, "스토리기반 저작물에서 감정어 분류에 기반한 등장인물의 감정 성향 판단" 한국컴퓨터정보학회 18 (18): 131-138, 2013

3 Klimt, Bryan, "The enron corpus: A new dataset for email classification research" 217-226, 2004

4 Joachims, Thorsten, "Text categorization with support vector machines: Learning with many relevant features" 137-142, 1998

5 Demsar, Janez, "Statistical comparisons of classifiers over multiple data sets" 1-30, 2006

6 Douglas Turnbull, "Semantic Annotation and Retrieval of Music and Sound Effects" Institute of Electrical and Electronics Engineers (IEEE) 16 (16): 467-476, 2008

7 S. Diplaris, "Protein Classification with Multiple Algorithms" 448-456, 2005

8 Ueda, Naonori, "Parametric mixture models for multi-labeled text" 737-744, 2003

9 I. Katakis, "Multilabel Text Classification for Automated Tag Suggestion" 2008

10 Wang, Tinghuai, "Multi-label propagation for coherent video segmentation and artistic stylization" 3005-3008, 2010

11 L. Enrique Sucar, "Multi-label classification with Bayesian network-based chain classifiers" Elsevier BV 41 : 14-22, 2014

12 Trohidis, Konstantinos, "Multi-Label Classification of Music into Emotions" 8 : 325-330, 2008

13 Persing, Isaac, "Modeling thesis clarity in student essays" 1 : 260-269, 2013

14 Min-Ling Zhang, "ML-KNN: A lazy learning approach to multi-label learning" Elsevier BV 40 (40): 2038-2048, 2007

15 Matthew R. Boutell, "Learning multi-label scene classification" Elsevier BV 37 (37): 1757-1771, 2004

16 Alberto Cano, "LAIM discretization for multi-label data" Elsevier BV 330 : 370-384, 2016

17 Barutcuoglu, Zafer, "Hierarchical multi-label prediction of gene function" 22 (22): 830-836, 2006

18 Min-Ling Zhang, "Feature selection for multi-label naive Bayes classification" Elsevier BV 179 (179): 3218-3229, 2009

19 Godbole, Shantanu, "Discriminative methods for multi-labeled classification" 22-30, 2004

20 A. Srivastava, "Discovering recurring anomalies in text reports regarding complex space systems" 3853-3862, 2005

21 Read, Jesse, "Classifier chains for multi-label classification" 254-269, 2009

22 Jesse Read, "Classifier chains for multi-label classification" Springer Nature 85 (85): 333-359, 2011

23 Jaedong Lee, "An approach for multi-label classification by directed acyclic graph with label correlation maximization" Elsevier BV 351 : 101-114, 2016

24 J. Pestian, "A shared task involving multi-label classification of clinical free text" 97-104, 2007

25 Elisseeff, Andre, "A kernel method for multi-labelled classification," Advances in neural information processing systems" 681-687, 2002

26 Min-Ling Zhang, "A Review on Multi-Label Learning Algorithms" Institute of Electrical and Electronics Engineers (IEEE) 26 (26): 1819-1837, 2014

27 Min-Ling Zhang, "Lift: Multi-Label Learning with Label-Specific Features" Institute of Electrical and Electronics Engineers (IEEE) 37 (37): 107-120, 2015