ML-KNN: A lazy learning approach to multi-label learning

By Min-Ling Zhang, Zhi-Hua Zhou. 2007

3,483 citations recorded by the source corpus.

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Citations in the corpus

Elucidating image-to-set prediction: An analysis of models, losses and datasets (2019, background): “Multi-label classification has been a long lasting problem in computer vision [58, 16, 59, 54].”
Transductive Multi-View Zero-Shot Learning (2015, methodology): “However, this does not exploit the multi-label correlations and thus has very limited expressive power [5, 33].”
Transductive Multilabel Learning via Label Set Propagation (2013, background): “X. Kong and Z.-H. Zhou are with the National Key Laboratory for Novel Software Technology, Nanjing University, Mailbox 603, 163 Xianlin Avenue, Nanjing 210023, China.”
A Literature Survey on Algorithms for Multi-label Learning (2010, methodology): “It is easy to note that, in that case, hamming loss is 2 k times of the classification error [66].”
Multi-label learning by exploiting label dependency (2010, methodology): “As shown in Table 7, for data sets with regular number of examples (|S| < 5000), Lead is significantly superior to the compared algorithms in 31.4% (Bsvm), 31.4% (Ml-knn), 68.6% (Bp-mll) and 54.3% (Ecc) cases, and is inferior to them in much less 0.0% (Bsvm), 17.1% (Mlknn), 8.6% (Bp-mll) and 17.1% (”
Multi-Label Classification of Music into Emotions (2008, methodology): “We compared the following multilabel classiﬁcation algorithms: binary relevance (BR), label powerset (LP), random k -labelsets (RAKEL) [15] and multilabel k-nearest neighbor (ML k NN) [21].”
Active Learning by Querying Informative and Representative Examples (2010, background): “Image is a data set for natural scene image classification, and contains 2000 images [52].”
Classifier chains for multi-label classification (2009, methodology): “Well-known approaches include AdaBoost [29], decision trees [35], and lazy methods [39].”
Semi-supervised Robust Dictionary Learning via Efficient l-Norms Minimization (2013, background): “Classification performance (definitions of the performance metrics can be found in [17]) comparison on TRECVID 2005 data.”
Hypergraph spectral learning for multi-label classification (2008, background): “Various approaches have been proposed in the past to exploit the correlation information contained in multiple labels [15, 16, 20, 21, 22, 26, 27].”
Block-Row Sparse Multiview Multilabel Learning for Image Classification (2016, methodology): “The popular learning techniques include lazy learning [43], kernel techniques [44], information-theoretic techniques [42], etc.”
Deep Learning for Extreme Multi-label Text Classification (2017, background): “Addressing the limitations of those traditional classication methods by explicitly modeling the dependencies or correlations among class labels has been the major focus of multi-label classication research [7, 11, 13, 15, 42, 48]; however, scalable solutions for problems with hundreds of thousands”
Multilabel Classification with Principal Label Space Transformation (2012, neutral): “MH (Schapire & Singer, 2000), Rank-SVM (Elisseeﬀ & Weston, 2001), Multi-label C4.5 (Clare & King, 2001) and ML-KNN (Zhang & Zhou, 2007).”
Scalable and efficient multi-label classification for evolving data streams (2012, neutral): “A number of single-label classifiers have also been adapted directly to multi-label classification, most notably decision trees (Clare and King 2001), k-nearest neighbor (Zhang and Zhou 2007) and boosting (Schapire and Singer 2000).”
Multi-task deep neural network for multi-label learning (2013, background): “Multi-Label Learning Multi-label learning ([7], [9], [10], [11]) is a special kind of supervised learning where each instance can belong to multiple classes.”
Learning shared subspace for multi-label dimensionality reduction via dependence maximization (2015, methodology): “The multilabel k-nearest neighbor classifier (MLkNN) [11] is then employed to predict the labels of each test sample.”
mlDEEPre: Multi-Functional Enzyme Function Prediction With Hierarchical Multi-Label Deep Learning (2019, methodology): “Furthermore, Zou and Xiao (2016) utilized ML-KNN and a diﬀerent feature extraction model, Grey Model (GM) (Lin et al., 2011), to perform the task.”