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.

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  1. Mena, Deiner; Montañés, Elena; Quevedo, José Ramón; del Coz, Juan José: A family of admissible heuristics for $\mathrm A^*$ to perform inference in probabilistic classifier chains (2017)
  2. Wu, Yu-Ping; Lin, Hsuan-Tien: Progressive random $k$-labelsets for cost-sensitive multi-label classification (2017)
  3. Varando, Gherardo; Bielza, Concha; Larrañaga, Pedro: Decision functions for chain classifiers based on Bayesian networks for multi-label classification (2016)
  4. Cobo, Luis C.; Subramanian, Kaushik; Isbell, Charles L.jun.; Lanterman, Aaron D.; Thomaz, Andrea L.: Abstraction from demonstration for efficient reinforcement learning in high-dimensional domains (2014)
  5. Waegeman, Willem; Dembczyński, Krzysztof; Jachnik, Arkadiusz; Cheng, Weiwei; Hüllermeier, Eyke: On the Bayes-optimality of F-measure maximizers (2014)
  6. Barranquero, Jose; González, Pablo; Díez, Jorge; Del Coz, Juan José: On the study of nearest neighbor algorithms for prevalence estimation in binary problems (2013)
  7. Gao, Wei; Zhou, Zhi-Hua: On the consistency of multi-label learning (2013)
  8. Liu, Huawen; Zheng, Zhonglong; Zhao, Jianmin; Ye, Ronghua: An ensemble method for high-dimensional multilabel data (2013)
  9. Yu, Ying; Pedrycz, Witold; Miao, Duoqian: Neighborhood rough sets based multi-label classification for automatic image annotation (2013)
  10. Zhou, Chunlai: Belief functions on distributive lattices (2013)
  11. Cesa-Bianchi, Nicolò; Re, Matteo; Valentini, Giorgio: Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference (2012)
  12. Ciarelli, Patrick Marques; Oliveira, Elias; Salles, Evandro O.T.: An incremental neural network with a reduced architecture (2012) ioport
  13. Denœux, Thierry; Masson, Marie-Hélène: Evidential reasoning in large partially ordered sets. Application to multi-label classification, ensemble clustering and preference aggregation (2012)
  14. He, Jianjun; Gu, Hong; Wang, Zhelong: Multi-instance multi-label learning based on Gaussian process with application to visual mobile robot navigation (2012) ioport
  15. He, Jianjun; Gu, Hong; Wang, Zhelong: Bayesian multi-instance multi-label learning using Gaussian process prior (2012)
  16. Kajdanowicz, Tomasz; Kazienko, Przemysław: Multi-label classification using error correcting output codes (2012) ioport
  17. Madjarov, Gjorgji; Gjorgjevikj, Dejan; Džeroski, Sašo: Two stage architecture for multi-label learning (2012) ioport
  18. Madjarov, Gjorgji; Kocev, Dragi; Gjorgjevikj, Dejan; Džeroski, Sašo: An extensive experimental comparison of methods for multi-label learning (2012) ioport
  19. Nock, Richard; Piro, Paolo; Nielsen, Frank; Ali, Wafa Bel Haj; Barlaud, Michel: Boosting $k$-NN for categorization of natural scenes (2012)
  20. Quevedo, José Ramón; Luaces, Oscar; Bahamonde, Antonio: Multilabel classifiers with a probabilistic thresholding strategy (2012)

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