ML-KNN

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. Iliadis, Dimitrios; De Baets, Bernard; Waegeman, Willem: Multi-target prediction for dummies using two-branch neural networks (2022)
  2. Bidgoli, Azam Asilian; Ebrahimpour-Komleh, Hossein; Rahnamayan, Shahryar: Reference-point-based multi-objective optimization algorithm with opposition-based voting scheme for multi-label feature selection (2021)
  3. Giunchiglia, Eleonora; Lukasiewicz, Thomas: Multi-label classification neural networks with hard logical constraints (2021)
  4. Huang, Jun; Xu, Linchuan; Qian, Kun; Wang, Jing; Yamanishi, Kenji: Multi-label learning with missing and completely unobserved labels (2021)
  5. Lyu, Gengyu; Feng, Songhe; Li, Yidong: Noisy label tolerance: a new perspective of partial multi-label learning (2021)
  6. Panos, Aristeidis; Dellaportas, Petros; Titsias, Michalis K.: Large scale multi-label learning using Gaussian processes (2021)
  7. Wang, Ran; Ye, Suhe; Li, Ke; Kwong, Sam: Bayesian network based label correlation analysis for multi-label classifier chain (2021)
  8. Xia, Yuelong; Chen, Ke; Yang, Yun: Multi-label classification with weighted classifier selection and stacked ensemble (2021)
  9. Zhang, Lei-Hong; Ma, Xijun; Shen, Chungen: A structure-exploiting nested Lanczos-type iteration for the multiview canonical correlation analysis (2021)
  10. Zhang, Ping; Gao, Wanfu; Hu, Juncheng; Li, Yonghao: Multi-label feature selection based on the division of label topics (2021)
  11. Zhou, Fan; Qi, Xiuxiu; Xiao, Chunjing; Wang, Jiahao: Metarisk: semi-supervised few-shot operational risk classification in banking industry (2021)
  12. Afridi, Mohammad Khan; Azam, Nouman; Yao, JingTao: Variance based three-way clustering approaches for handling overlapping clustering (2020)
  13. Chen, Yumin; Miao, Duoqian: Granular regression with a gradient descent method (2020)
  14. Che, Xiaoya; Chen, Degang; Mi, Jusheng: A novel approach for learning label correlation with application to feature selection of multi-label data (2020)
  15. Kocev, Dragi; Ceci, Michelangelo; Stepišnik, Tomaž: Ensembles of extremely randomized predictive clustering trees for predicting structured outputs (2020)
  16. Tan, Zhi-Hao; Tan, Peng; Jiang, Yuan; Zhou, Zhi-Hua: Multi-label optimal margin distribution machine (2020)
  17. Wu, Guoqiang; Zheng, Ruobing; Tian, Yingjie; Liu, Dalian: Joint ranking SVM and binary relevance with robust low-rank learning for multi-label classification (2020)
  18. Yang, Bo; Tong, Kunkun; Zhao, Xueqing; Pang, Shanmin; Chen, Jinguang: Multilabel classification using low-rank decomposition (2020)
  19. Do, Kien; Tran, Truyen; Nguyen, Thin; Venkatesh, Svetha: Attentional multilabel learning over graphs: a message passing approach (2019)
  20. Sadinle, Mauricio; Lei, Jing; Wasserman, Larry: Least ambiguous set-valued classifiers with bounded error levels (2019)

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