LPIS Home Page
Google Search

Title: Mulan: A Java Library for Multi-Label Learning
Author(s): G. Tsoumakas, E. Spyromitros-Xioufis, J. Vilcek, I. Vlahavas.
Availability: Click here to download the PDF (Acrobat Reader) file (4 pages).
Keywords:
Appeared in: Journal of Machine Learning Research, 12, pp. 2411-2414, 2011.
Abstract: Mulan is a Java library for learning from multi-label data. It offers a variety of classiffication, ranking, thresholding and dimensionality reduction algorithms, including an algorithm for learning from hierarchically structured labels. In addition, it contains an evaluation framework that calculates a rich variety of performance measures.
See also :


        This paper has been cited by the following:

1 C. Sanden, “An empirical evaluation of computational and perceptual multi-label genre classification on music,” Lethbridge, Alta.: University of Lethbridge, Dept. of Mathematics and Computer Science, c2010, 2010.
2 M. van Leeuwen, “Maximal exceptions with minimal descriptions,” Data Mining and Knowledge Discovery, vol. 21, no. 2, pp. 259–276, 2010.
3 C. Sanden and J. Z. Zhang, “Enhancing multi-label music genre classification through ensemble techniques,” in Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, 2011, pp. 705–714.
4 H. Sajnani, S. Javanmardi, D. W. McDonald, and C. V. Lopes, “Multi-Label Classification of Short Text: A Study on Wikipedia Barnstars.,” in Analyzing Microtext, 2011.
5 M. Ogrodniczuk and D. Karagiozov, “ATLAS–The multilingual language processing platform,” 2011.
6 D. W. McDonald, S. Javanmardi, and M. Zachry, “Finding patterns in behavioral observations by automatically labeling forms of wikiwork in barnstars,” in Proceedings of the 7th International Symposium on Wikis and Open Collaboration, 2011, pp. 15–24.
7 M. I. Mandel, R. Pascanu, D. Eck, Y. Bengio, L. M. Aiello, R. Schifanella, and F. Menczer, “Contextual tag inference,” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP), vol. 7, no. 1, p. 32, 2011.
8 H. S. S. Javanmardi, D. W. McDonald, and C. V. Lopes, “Multi-Label Classification of Short Text: A Study on Wikipedia Barnstars,” 2011.
9 I. Birzniece and M. Kirikova, “Interactive Inductive Learning: Application in Domain of Education,” 2011.
10 I. Birzniece, “Artificial Intelligence in Knowledge Management: Overview and Trends,” Scientific Journal of Riga Technical University. Computer Sciences, vol. 43, no. 1, pp. 5–11, 2011.
11 V. F. L. Batista, F. P. Pintado, A. B. Gil, S. Rodriguez, and M. N. Moreno, “A System for Multi-label Classification of Learning Objects,” in Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011, 2011,
12 G. Lastra, O. Luaces, J. R. Quevedo, and A. Bahamonde, “Graphical feature selection for multilabel classification tasks,” in Advances in Intelligent Data Analysis X, Springer, 2011, pp. 246–257.
13 Y. Yang and S. Gopal, “Multilabel classification with meta-level features in a learning-to-rank framework,” Machine learning, vol. 88, no. 1–2, pp. 47–68, 2012.
14 V. F. Lopez, F. de la Prieta, M. Ogihara, and D. D. Wong, “A model for multi-label classification and ranking of learning objects,” Expert Systems with Applications, vol. 39, no. 10, pp. 8878–8884, 2012.
15 T. Zhou, D. Tao, and X. Wu, “Compressed labeling on distilled labelsets for multi-label learning,” Machine learning, vol. 88, no. 1–2, pp. 69–126, 2012.
16 O. Reyes, C. Morell, and S. Ventura, “Learning similarity metric to improve the performance of lazy multi-label ranking algorithms,” in Intelligent Systems Design and Applications (ISDA), 2012 12th International Conference on, 2012, pp. 246–251.
17 A. Soylu, F. Modritscher, F. Wild, P. De Causmaecker, and P. Desmet, “Mashups by orchestration and widget-based personal environments: Key challenges, solution strategies, and an application,” Program: electronic library and information systems, vol. 46,
18 F. Tai and H.-T. Lin, “Multilabel classification with principal label space transformation,” Neural Computation, vol. 24, no. 9, pp. 2508–2542, 2012.
19 P. E. Taylor, G. J. Almeida, J. K. Hodgins, and T. Kanade, “Multi-label classification for the analysis of human motion quality,” in Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, 2012, pp. 2214–2218.
20 C.-Y. Zhao and J. Si, “Semi-supervised multi-label Boosting algorithm,” Jisuanji Yingyong Yanjiu, vol. 29, no. 9, pp. 3266–3268, 2012.
21 M. van Leeuwen and A. Knobbe, “Diverse subgroup set discovery,” Data Mining and Knowledge Discovery, vol. 25, no. 2, pp. 208–242, 2012.
22 J. Lee, H. Lim, and D.-W. Kim, “Approximating mutual information for multi-label feature selection,” Electronics letters, vol. 48, no. 15, pp. 929–930, 2012.
23 J. Ramon Quevedo, O. Luaces, and A. Bahamonde, “Multilabel classifiers with a probabilistic thresholding strategy,” Pattern Recognition, vol. 45, no. 2, pp. 876–883, 2012.
24 N. Thien, “Text Classification of Technical Papers Using Text Segmentation,” 2012.
25 D. B. Core, “Applications of Text Classification to Enterprise Support Documents,” 2012.
26 J. S. Saleema, B. Sairam, S. D. Naveen, K. Yuvaraj, and L. M. Patnaik, “Prominent label identification and multi-label classification for cancer prognosis prediction,” in TENCON 2012-2012 IEEE Region 10 Conference, 2012, pp. 1–6.
27 A. Kumar, S. Vembu, A. K. Menon, and C. Elkan, “Learning and inference in probabilistic classifier chains with beam search,” in Machine Learning and Knowledge Discovery in Databases, Springer, 2012, pp. 665–680.
28 K. Basu, V. Debusschere, and S. Bacha, “Load identification from power recordings at meter panel in residential households,” in Electrical Machines (ICEM), 2012 XXth International Conference on, 2012, pp. 2098–2104.
29 Y.-N. Chen and H.-T. Lin, “Feature-aware Label Space Dimension Reduction for Multi-label Classification,” in Advances in Neural Information Processing Systems, 2012, pp. 1538–1546.
30 P. P. da Gama, F. C. Bernardini, and B. Zadrozny, “Rb: A new method for constructing multi-label classifiers based on random selection and bagging,” Learning and Nonlinear Models, 2012.
31 L. De Ferrari, S. Aitken, J. van Hemert, and I. Goryanin, “EnzML: multi-label prediction of enzyme classes using InterPro signatures,” BMC bioinformatics, vol. 13, no. 1, p. 61, 2012.
32 P. J. Donnelly, “Bayesian Approaches to Musical Instrument Classification using Timbre Segmentation,” MONTANA STATE UNIVERSITY Bozeman, 2012.
33 B. Fu, Z. Wang, R. Pan, G. Xu, and P. Dolog, “Learning tree structure of label dependency for multi-label learning,” in Advances in Knowledge Discovery and Data Mining, Springer, 2012, pp. 159–170.
34 J. Furnkranz and S.-H. Park, “Error-Correcting Output Codes as a Transformation from Multi-Class to Multi-Label Prediction,” in Discovery Science, 2012, pp. 254–267.
35 S. Hammerl, T. Hermann, and H. Ritter, “Towards a semi-automatic personal digital diary: detecting daily activities from smartphone sensors,” in Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments, 2
36 I. Katakis, G. Pallis, M. D. Dikaiakos, and O. Onoufriou, “Automated Tagging for the Retrieval of Software Resources in Grid and Cloud Infrastructures,” in Cluster, Cloud and Grid Computing (CCGrid), 2012 12th IEEE/ACM International Symposium on, 2012, pp
37 F. Charte, A. Rivera, M. J. del Jesus, and F. Herrera, “Improving multi-label classifiers via label reduction with association rules,” in Hybrid Artificial Intelligent Systems, Springer, 2012, pp. 188–199.
38 Y. Yu, W. Pedrycz, D. Miao, and H. Zhang, “Neighborhood Rough Sets based Multi-label Classification,” in IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint, 2013, pp. 86–90.
39 T. H. Nguyen and K. Shirai, “Text Classification of Technical Papers Based on Text Segmentation,” in Natural Language Processing and Information Systems, Springer, 2013, pp. 278–284.
40 A. Rios and R. Kavuluru, “Supervised Extraction of Diagnosis Codes from EMRs: Role of Feature Selection, Data Selection, and Probabilistic Thresholding,” in Healthcare Informatics (ICHI), 2013 IEEE International Conference on, 2013, pp. 66–73.
41 S. Otalora-Montenegro, S. A. Perez-Rubiano, and F. A. Gonzalez, “Online Matrix Factorization for Space Embedding Multilabel Annotation,” in Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Springer, 2013, pp. 343–350.
42 I. Pillai, G. Fumera, and F. Roli, “Multi-label classification with a reject option,” Pattern Recognition, 2013.
43 R. C. Prati and F. Olivetti de Franca, “Extending features for multilabel classification with swarm biclustering,” in Evolutionary Computation (CEC), 2013 IEEE Congress on, 2013, pp. 2964–2971.
44 O. G. R. Pupo, C. Morell, and S. V. Soto, “ReliefF-ML: An Extension of ReliefF Algorithm to Multi-label Learning,” in Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Springer, 2013, pp. 528–535.
45 F. J. Ribadas, L. M. de Campos, V. M. Darriba, and A. E. Romero, “Two hierarchical text categorization approaches for BioASQ semantic indexing challenge,” in 1st BioASQ Workshop: A challenge on large-scale biomedical semantic indexing and question answeri
46 A. Rios, R. Vanderpool, P. Shaw, and R. Kavuluru, “A Multi-Label Classification Approach for Coding Cancer Information Service Chat Transcripts,” in The Twenty-Sixth International FLAIRS Conference, 2013.
47 J. D. Rodr?guez, “Advances in Error Estimation and Multi-Dimensional Supervised Classification,” University of the Basque Country, 2013.
48 N. Spolaor, E. A. Cherman, M. C. Monard, and H. D. Lee, “A Comparison of Multi-label Feature Selection Methods using the Problem Transformation Approach,” Electronic Notes in Theoretical Computer Science, vol. 292, pp. 135–151, 2013.
49 M. A. Tahir, A. Bouridane, and J. Kittler, “Dimensionality Reduction Using Stacked Kernel Discriminant Analysis for Multi-label Classification,” in Multiple Classifier Systems, Springer, 2013, pp. 283–294.
50 C. A. Tawiah and V. S. Sheng, “A study on multi-label classification,” in Advances in Data Mining. Applications and Theoretical Aspects, Springer, 2013, pp. 137–150.
51 X. Xia, D. Lo, X. Wang, and B. Zhou, “Tag recommendation in software information sites,” in Proceedings of the Tenth International Workshop on Mining Software Repositories, 2013, pp. 287–296.
52 M.-L. Zhang and Z.-H. Zhou, “A Review On Multi-Label Learning Algorithms,” IEEE Transactions on Knowledge and Data Engineering, vol. 99, no. PrePrints, p. 1, 2013.
53 H. Nair, K. Shujaee, D. Krooks, and C. Armstrong, “Automated Annotation of Text Using the Classification-based Annotation Workbench (CLAW),” in IN?LI 2013, The Second International Conference on Intelligent Systems and Applications, 2013, pp. 6–11.
54 L. Wu and M.-L. Zhang, “Multi-Label Classification with Unlabeled Data: An Inductive Approach,” in Asian Conference on Machine Learning, 2013, pp. 197–212.
55 A. F. Giraldo-Forero, J. A. Jaramillo-Garzon, J. F. Ruiz-Munoz, and C. G. Castellanos-Dominguez, “Managing Imbalanced Data Sets in Multi-label Problems: A Case Study with the SMOTE Algorithm,” in Progress in Pattern Recognition, Image Analysis, Computer V
56 P. Bermejo, M. Lucas, J. A. Rodriguez-Montes, P. J. Tarraga, J. Lucas, J. A. Gamez, and J. M. Puerta, “Single-and Multi-label Prediction of Burden on Families of Schizophrenia Patients,” in Artificial Intelligence in Medicine, Springer, 2013, pp. 115–124.
57 J. Cheng, “Mixed and Covariate Dependent Graphical Models,” The University of Michigan, 2013.
58 J. Cheng, E. Levina, and J. Zhu, “High-dimensional Mixed Graphical Models,” arXiv preprint arXiv:1304.2810, 2013.
59 W. Duivesteijn, “Exceptional model mining,” Leiden Institute of Advanced Computer Science (LIACS), Faculty of Science, Leiden University, 2013.
60 F. Briggs, Y. Huang, R. Raich, K. Eftaxias, Z. Lei, W. Cukierski, S. F. Hadley, A. Hadley, M. Betts, and X. Z. Fern, “The 9th annual MLSP competition: New methods for acoustic classification of multiple simultaneous bird species in a noisy environment,” i
61 M. J. Flores, J. A. Gamez, and A. M. Martinez, “Domains of competence of the semi-naive Bayesian network classifiers,” Information Sciences, 2013.
62 E. Loza Mencia, J. Nam, and D.-H. Lee, “Learning multi-labeled bioacoustic samples with an unsupervised feature learning approach,” in Proceedings of International Symposium on Neural Information Scaled for Bioacoustics joint to NIPS, 2013, pp. 116–121.
63 D. Gjorgjevikj, G. Madjarov, and S. D\vzeroski, “Hybrid Decision Tree Architecture utilizing Local SVMs for Efficient Multi-Label Learning,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 27, no. 07, 2013.
64 E. C. Gon\ccalves, A. Plastino, and A. A. Freitas, “A Genetic Algorithm for Optimizing the Label Ordering in Multi-Label Classifier Chains.,” in Proceedings of the 2013 25th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), 2013
65 T. GRILLETTA, L. MANGEONJEAN, and B. SEGAULT, “Classement multilabels de signaux cerebraux pour le controle d’un bras robotique Projet Interdisciplinaire de Decouverte de la Recherche,” 2013.
66 D. Heath and D. Ventura, “IMPROVING MULTILABEL CLASSIFICATION BY AVOIDING IMPLICIT NEGATIVITY WITH INCOMPLETE DATA,” Computational Intelligence, 2013.
67 A. Kumar, S. Vembu, A. K. Menon, and C. Elkan, “Beam search algorithms for multilabel learning,” Machine Learning, pp. 1–25, 2013.
68 S.-J. Lee and J.-Y. Jiang, “Multi-Label Text Categorization Based on Fuzzy Relevance Clustering,” 2013.
69 N. Li and Z.-H. Zhou, “Selective Ensemble of Classifier Chains,” in Multiple Classifier Systems, Springer, 2013, pp. 146–156.
70 H. Lo, S. Lin, and H.-M. Wang, “Generalized k-Labelsets Ensemble for Multi-Label and Cost-Sensitive Classification,” 2013.
71 C.-S. Ferng and H.-T. Lin, “Multilabel Classification Using Error-Correcting Codes of Hard or Soft Bits,” IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 11, pp. 1888–1900, 2013.
72 Y. Yu, W. Pedrycz, and D. Miao, “Multi-label classification by exploiting label correlations,” Expert Systems with Applications, vol. 41, no. 6, pp. 2989–3004, 2014.
73 B. Fu, Z. Wang, G. Xu, and L. Cao, “Multi-label Learning Based on Iterative Label Propagation over Graph,” Pattern Recognition Letters, 2014.
74 F. Markatopoulou, V. Mezaris, and I. Kompatsiaris, “A Comparative Study on the Use of Multi-label Classification Techniques for Concept-Based Video Indexing and Annotation,” in MultiMedia Modeling, 2014, pp. 1–12.
75 P. Naula, A. Airola, T. Salakoski, and T. Pahikkala, “Multi-label learning under feature extraction budgets,” Pattern Recognition Letters, vol. 40, pp. 56–65, 2014.


MLKD Home ISKP Home