[1] ZHANG M L, ZHOU Z H. A review on multi-label learning algorithms[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(8): 1819-1837. [2] READ J, PFAHRINGER B, HOLMES G, et al. Classifier chains for multi-label classification[J]. Machine Learning, 2011, 85(3): 333-359. [3] ZAHARIA M, CHOWDHURY M, DAS T, et al. Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing[C]// Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation. San Hose, USA: USENIX Association, 2012: 141-146. [4] ZHU B, MARA A, MOZO A. CLUS: Parallel subspace clustering algorithm on spark[A]// New Trends in Databases and Information Systems[M]. Springer, 2015, 539:175-185. [5] MAILLO J, RAMREZ S, TRIGUERO I, et al. kNN-IS: An iterative spark-based design of the k-nearest neighbors classifier for big data[J]. Knowledge-Based Systems, 2016, 117: 3-15; doi: 10.1016/j.knosys.2016.06.012. [6] KIM H, PARK J, JANG J, et al. DeepSpark: Spark-based deep learning supporting asynchronous updates and Caffe compatibility[J/OL]. https://arxiv.org/abs/1602.08191v1,2016.03.08, 2016: arXiv:1602.08191v1. [7] DUAN M X, LI K L, TANG Z, et al. Selection and replacement algorithms for memory performance improvement in Spark[J]. Concurrency & Computation Practice & Experience, 2015, 28(8): 2473-2486. [8] TSOUMAKAS G, KATAKIS I, VLAHAVAS I. Mining multi-label data[A]// Data Mining and Knowledge Discovery Handbook[M]. Springer, 2009: 667-685. [9] READ J, PFAHRINGER B, HOLMES G. Multi-label classification using ensembles of pruned sets[C]// Proceedings of the 8th International Conference on Data Mining. Pisa, Italy: IEEE Computer Society, 2008: 995-1000. [10] TSOUMAKAS G, SPYROMITROS-XIOUFIS E, VILCEK J, et al. Mulan: A Java library for multi-label learning[J]. Journal of Machine Learning Research, 2011, 12(2): 2411-2414. [11] MENCA E L, FRNKRANZ J. Efficient pairwise multilabel classification for large-scale problems in the legal domain[C]// European Conference on Machine Learning & Knowledge Discovery in Databases. Antwerp, Belgium: Springer, 2008: 50-65. [12] SNOEK C G M, WORRING M, VAN GEMERT J C, et al. The challenge problem for automated detection of 101 semantic concepts in multimedia[C]// Proceedings of the 14th ACM International Conference on Multimedia. Santa Barbara, USA: ACM Press. 2006: 421-430. [13] CHUA T S, TANG J, HONG R, et al. NUS-WIDE: A real-world web image database from National University of Singapore[C]// Proceedings of the ACM International Conference on Image and Video Retrieval. Santorini, Greece: ACM Press, 2009: doi:10.1145/1646396.1646452. [14] SPYROMITROS-XIOUFIS E, PAPADOPOULOS S, KOMPATSIARIS I Y, et al. A comprehensive study over VLAD and product quantization in large-scale image retrieval[J]. IEEE Transactions on Multimedia, 2014, 16(6): 1713-1728. [15] ZHANG M L, WU L. LIFT: Multi-label learning with label-specific features[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 37(1): 107-20. [16] FRNKRANZ J, HLLERMEIER E, MENCA E L, et al. Multilabel classification via calibrated label ranking[J]. Machine Learning, 2008, 73(2): 133-153. [17] TSOUMAKAS G, KATAKIS I, VLAHAVAS I. Random k-labelsets for multilabel classification[J]. IEEE Transactions on Knowledge & Data Engineering, 2011, 23(7): 1079-1089. |