Feature selection for multi-label naive Bayes classification

By Min-Ling Zhang, José María Peña Sánchez, Victor Robles. Information Sciences, 2009

505 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/4bd57a4e568901b8061a946c934af722ffb55d3a

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Citations in the corpus

Efficient max-margin multi-label classification with applications to zero-shot learning (2012, background): “…Katakis 2007; Ueda and Saito 2003), multi-class (Dekel and Shamir 2010) and ordinal (Cheng et al. 2010) multi-label classiﬁcation as well as semi-supervised learning, feature selection (Zhang and Wang 2009b), active learning (Li et al. 2004), multi-instance learning (Zhang and Wang 2009a), etc .”
A review of feature selection methods on synthetic data (2013, methodology): “Similarly, there are works examining different FS methods to obtain good performance results using a specific classifier (naive Bayes in [32], C4.”
mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification (2011, background): “In the classification phase, a point x is assigned a label y according to y = sgn[w x + b].”
A Review on Multi-Label Learning Algorithms (2014, background): “There are some other wrapper-style approaches which conduct dimensionality reduction by combining with filter-style techniques [187] or unlabeled data [114].”
Strengthening learning algorithms by feature discovery (2012, background): “Feature selection methods are able to increase classification performance by removing confusing or irrelevant features [27,30] and result in more comprehensible hypothesis and smaller rule sets [17].”
Multi-label learning with millions of labels: recommending advertiser bid phrases for web pages (2013, background): “Almost all the multi-label classifiers that have been proposed in the literature have prediction costs that are at least linear in the number of labels [8,19–22,24,38–42,47].”