# multi label classification in MACHINE LEARNING

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In machine learning, multi-label classification and the strongly related problem of multi-output classification are variants of the classification problem where multiple labels may be assigned to each instance.

** A k-nearest neighbor based algorithm for multi – label classification .**

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is addressed, where a k-nearest neighbor based method for multi – label classification named ML of the 16th International Conference on Machine Learning (ICML99), Bled, Slovenia, 199 pp profiles to learn functional categories using support vector machines , in Proceedings

** Meka: a multi-label/multi-target extension to weka**

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For example, to run five fold cross validation of an ensemble of 50 chain classifiers (Read et al., 2011) on the Music data set with support vector machines as the base classifier (as also shown Classifier chains for multi – label classification . Machine Learning , 85(3):333 359

** Multi – label classification on tree-and dag-structured hierarchies**

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Many real-world applications involve multilabel classification, in which the labels are organized in the form of a tree or directed acyclic graph (DAG). However, current research efforts typically ignore the label dependencies or can only exploit the dependencies in tree

** Efficient multi – label classification with many labels**

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In multi – label classification , each sample can be associated with a set of class labels. When the number of labels grows to the hundreds or even thousands, existing multi – label classification methods often become computationally inefficient. In recent years, a number of

** From softmax to sparsemax: A sparse model of attention and multi – label classification **

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We propose sparsemax, a new activation function similar to the traditional softmax, but able to output sparse probabilities. After deriving its properties, we show how its Jacobian can be efficiently computed, enabling its use in a network trained with backpropagation. Then, we

** Large-scale multi-label learning with missing labels**

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Large scale multi – label classification is an important learn- ing problem with several applications to real-world prob- lems such as image/video annotation (Carneiro et al., Proceedings of the 31st International Conference on Machine Learning , Beijing, China

** On the consistency of multi-label learning **

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For hamming loss, we show that some recent multi-label learning approaches are inconsistent even for deterministic multi – label classification , and give a surrogate loss function which is consistent for the deterministic case. Finally

** Semi-supervised multi-label learning by constrained non-negative matrix factorization**

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learning . This is because by enforcing examples with similar input patterns to share similar sets of class la- bels, we essentially propagate the class labels through the similarity graph of examples, which is the key idea of the label propagation approaches. A number of machine

** Multi – label classification with label constraints**

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testing process. 5 Discussion We introduced constraints into the multi – label classification setting, and studied two machine learning tasks in this context: 1. Integration of additional knowledge in form of label constraints into the

** Multi-label learning by instance differentiation**

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Collective multi – label classification 897 904. Joachims, T. 1998. Text categorization with support vec- tor machines : learning with many relevant features. In Proceedings of the 10th European Conference on Machine Learning , 137 142

** Multi – label classification methods for multi-target regression**

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cific learning approach (eg k nearest neighbors, decision tree, support vector machine ) for handling data mainly depends on how easy it is for the underlying learning algorithm to as no surprise that there exist decision tree algorithms for both multi – label classification and

** Active learning for multi-label image annotation**

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present query selection strategies for active learning for multi – label classification . A note on Platts probabilistic outputs for support vector machines . Machine Learning

** Optimizing the F-measure in multi – label classification : Plug-in rule approach versus structured loss minimization**

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We compare the plug-in rule approach for optimizing the Fβ-measure in multi – label classification with an approach based on structured loss minimization, such as the structured support vector machine (SSVM). Whereas the former derives an optimal prediction from a

** An Empirical Study of Multi-label Learning Methods for Video Annotation.**

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Multi – label classification : An overview Duin, and J. Matas, On com- bining classifiers, IEEE Trans Pattern Analysis and Machine Intelligence , Using support vector machines for classifying

** Correlation-based pruning of stacked binary relevance models for multi-label learning **

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part of their SVM-HF method, which was based on a support vector machine (SVM) algorithm binary output of these models we can obtain a bipartition of the labels ( multi – label classification ), while if 0.74 0.75 0.76 t Average Precision DT LR SVM (b) Support vector machines Fig

** A ranking-based KNN approach for multi – label classification **

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Multi – label classification has attracted a great deal of attention in recent years. This paper presents an interesting finding, namely, being able to identify neighbors with trustable labels can significantly improve the classification accuracy. Based on this finding, we propose a k

** Conditional bernoulli mixtures for multi – label classification **

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Multi – label classification is an important machine learning task wherein one assigns a subset of candidate labels to an object. In this paper, we propose a new multi – label classification method based on Conditional Bernoulli Mixtures. Our proposed method has

** Multilabel text classification for automated tag suggestion**

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a boosting-based system for text catego- rization. Machine Learning Multi – label classification : An overview. International Journal of Data Warehousing and Mining

** Condensed filter tree for cost-sensitive multi – label classification **

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Different real-world applications of multi – label classification often demand different evaluation criteria. We formalize this demand with a general setup, cost-sensitive multi – label classification (CSMLC), which takes the evaluation criteria into account during learning

** Multi-label output codes using canonical correlation analysis**

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Using canonical correlation analysis, we pro- pose an error-correcting code for multi – label classification code and research areas such as com- pressed sensing and ensemble learning in Proceedings of the 14th International Con- ference on Artificial Intelligence and Statistics