Book ChapterDOI
Mining Multi-label Data
Grigorios Tsoumakas,Ioannis Katakis,Ioannis Vlahavas +2 more
- pp 667-685
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TLDR
A large body of research in supervised learning deals with the analysis of single-label data, where training examples are associated with a single label λ from a set of disjoint labels L, however, training examples in several application domains are often associated withA set of labels Y ⊆ L.Abstract:
A large body of research in supervised learning deals with the analysis of single-label data, where training examples are associated with a single label λ from a set of disjoint labels L. However, training examples in several application domains are often associated with a set of labels Y ⊆ L. Such data are called multi-label.read more
Citations
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Journal ArticleDOI
A Review On Multi-Label Learning Algorithms
Min-Ling Zhang,Zhi-Hua Zhou +1 more
TL;DR: This paper aims to provide a timely review on this area with emphasis on state-of-the-art multi-label learning algorithms with relevant analyses and discussions.
Journal ArticleDOI
An extensive experimental comparison of methods for multi-label learning
TL;DR: The results of the analysis show that for multi-label classification the best performing methods overall are random forests of predictive clustering trees (RF-PCT) and hierarchy of multi- label classifiers (HOMER), followed by binary relevance (BR) and classifier chains (CC).
Journal Article
MULAN: A Java Library for Multi-Label Learning
TL;DR: MULAN is a Java library for learning from multi-label data that offers a variety of classification, ranking, thresholding and dimensionality reduction algorithms, as well as algorithms forlearning from hierarchically structured labels.
Journal ArticleDOI
Text Classification Algorithms: A Survey
Kamran Kowsari,Kiana Jafari Meimandi,Mojtaba Heidarysafa,Sanjana Mendu,Laura E. Barnes,Donald E. Brown +5 more
TL;DR: A brief overview of text classification algorithms is discussed in this article, where different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods are discussed, and the limitations of each technique and their application in real-world problems are discussed.
Journal ArticleDOI
Text Classification Algorithms: A Survey
Kamran Kowsari,Kiana Jafari Meimandi,Mojtaba Heidarysafa,Sanjana Mendu,Laura E. Barnes,Donald E. Brown +5 more
TL;DR: An overview of text classification algorithms is discussed, which covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods.
References
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LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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Wrappers for feature subset selection
Ron Kohavi,George H. John +1 more
TL;DR: The wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain and compares the wrapper approach to induction without feature subset selection and to Relief, a filter approach tofeature subset selection.
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Original Contribution: Stacked generalization
TL;DR: The conclusion is that for almost any real-world generalization problem one should use some version of stacked generalization to minimize the generalization error rate.
Proceedings Article
A Comparative Study on Feature Selection in Text Categorization
Yiming Yang,Jan O. Pedersen +1 more
TL;DR: This paper finds strong correlations between the DF IG and CHI values of a term and suggests that DF thresholding the simplest method with the lowest cost in computation can be reliably used instead of IG or CHI when the computation of these measures are too expensive.
Journal ArticleDOI
The Gene Ontology (GO) database and informatics resource.
Midori A. Harris,Jennifer I. Clark,Ireland A,Jane Lomax,Michael Ashburner,Michael Ashburner,R. Foulger,R. Foulger,Karen Eilbeck,Karen Eilbeck,Suzanna E. Lewis,Suzanna E. Lewis,B. Marshall,B. Marshall,Christopher J. Mungall,Christopher J. Mungall,J. Richter,J. Richter,Gerald M. Rubin,Gerald M. Rubin,Judith A. Blake,Carol J. Bult,Dolan M,Drabkin H,Janan T. Eppig,Hill Dp,L. Ni,Ringwald M,Rama Balakrishnan,Rama Balakrishnan,J. M. Cherry,J. M. Cherry,Karen R. Christie,Karen R. Christie,Maria C. Costanzo,Maria C. Costanzo,Selina S. Dwight,Selina S. Dwight,Stacia R. Engel,Stacia R. Engel,Dianna G. Fisk,Dianna G. Fisk,Jodi E. Hirschman,Jodi E. Hirschman,Eurie L. Hong,Eurie L. Hong,Robert S. Nash,Robert S. Nash,Anand Sethuraman,Anand Sethuraman,Chandra L. Theesfeld,Chandra L. Theesfeld,David Botstein,David Botstein,Kara Dolinski,Kara Dolinski,Becket Feierbach,Becket Feierbach,Tanya Z. Berardini,Tanya Z. Berardini,S. Mundodi,S. Mundodi,Seung Y. Rhee,Seung Y. Rhee,Rolf Apweiler,Daniel Barrell,Camon E,E. Dimmer,Lee,Rex L. Chisholm,Pascale Gaudet,Pascale Gaudet,Warren A. Kibbe,Warren A. Kibbe,Ranjana Kishore,Ranjana Kishore,Erich M. Schwarz,Erich M. Schwarz,Paul W. Sternberg,Paul W. Sternberg,M. Gwinn,Hannick L,Wortman J,Matthew Berriman,Matthew Berriman,Wood,Wood,de la Cruz N,de la Cruz N,Peter J. Tonellato,Peter J. Tonellato,Pankaj Jaiswal,Pankaj Jaiswal,Seigfried T,Seigfried T,White R,White R +96 more
TL;DR: The Gene Ontology (GO) project as discussed by the authors provides structured, controlled vocabularies and classifications that cover several domains of molecular and cellular biology and are freely available for community use in the annotation of genes, gene products and sequences.