Ml-knn: A lazy learning approach to multi-label learning. Multi-label learning originated from the investigation of text categorization problem, where each document may belong to several predefined topics simultaneously. In multi-label learning, the training set is composed of instances each associated with a set of labels, and the task is to predict the label sets of unseen instances through analyzing training instances with known label sets. In this paper, a multi-label lazy learning approach named ML-KNN is presented, which is derived from the traditional K-nearest neighbor (KNN) algorithm. In detail, for each unseen instance, its K nearest neighbors in the training set are firstly identified. After that, based on statistical information gained from the label sets of these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, maximum a posteriori (MAP) principle is utilized to determine the label set for the unseen instance. Experiments on three different real-world multi-label learning problems, i.e. Yeast gene functional analysis, natural scene classification and automatic web page categorization, show that ML-KNN achieves superior performance to some well-established multi-label learning algorithms.

References in zbMATH (referenced in 74 articles )

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  1. Gao, Wei; Zhou, Zhi-Hua: On the consistency of multi-label learning (2013)
  2. Liu, Huawen; Zheng, Zhonglong; Zhao, Jianmin; Ye, Ronghua: An ensemble method for high-dimensional multilabel data (2013)
  3. Yu, Ying; Pedrycz, Witold; Miao, Duoqian: Neighborhood rough sets based multi-label classification for automatic image annotation (2013)
  4. Zhou, Chunlai: Belief functions on distributive lattices (2013)
  5. Cesa-Bianchi, Nicolò; Re, Matteo; Valentini, Giorgio: Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference (2012)
  6. Ciarelli, Patrick Marques; Oliveira, Elias; Salles, Evandro O. T.: An incremental neural network with a reduced architecture (2012) ioport
  7. Denœux, Thierry; Masson, Marie-Hélène: Evidential reasoning in large partially ordered sets. Application to multi-label classification, ensemble clustering and preference aggregation (2012)
  8. He, Jianjun; Gu, Hong; Wang, Zhelong: Bayesian multi-instance multi-label learning using Gaussian process prior (2012)
  9. He, Jianjun; Gu, Hong; Wang, Zhelong: Multi-instance multi-label learning based on Gaussian process with application to visual mobile robot navigation (2012) ioport
  10. Kajdanowicz, Tomasz; Kazienko, Przemysław: Multi-label classification using error correcting output codes (2012) ioport
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  13. Nock, Richard; Piro, Paolo; Nielsen, Frank; Ali, Wafa Bel Haj; Barlaud, Michel: Boosting (k)-NN for categorization of natural scenes (2012)
  14. Quevedo, José Ramón; Luaces, Oscar; Bahamonde, Antonio: Multilabel classifiers with a probabilistic thresholding strategy (2012)
  15. Read, Jesse; Bifet, Albert; Holmes, Geoff; Pfahringer, Bernhard: Scalable and efficient multi-label classification for evolving data streams (2012) ioport
  16. Tahir, Muhammad Atif; Kittler, Josef; Yan, Fei: Inverse random under sampling for class imbalance problem and its application to multi-label classification (2012) ioport
  17. Tai, Farbound; Lin, Hsuan-Tien: Multilabel classification with principal label space transformation (2012)
  18. Yang, Yiming; Gopal, Siddharth: Multilabel classification with meta-level features in a learning-to-rank framework (2012)
  19. Zhou, Tianyi; Tao, Dacheng; Wu, Xindong: Compressed labeling on distilled labelsets for multi-label learning (2012)
  20. Zhou, Zhi-Hua; Zhang, Min-Ling; Huang, Sheng-Jun; Li, Yu-Feng: Multi-instance multi-label learning (2012)