Journal ArticleDOI
CCnet: Joint multi-label classification and feature selection using classifier chains and elastic net regularization
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TLDR
An algorithm CCnet is proposed which is a combination of classifier chains and elastic-net regularization and it is shown that the feature selection is stable with respect to the order of fitting the models in the chain.About:
This article is published in Neurocomputing.The article was published on 2017-04-26. It has received 20 citations till now. The article focuses on the topics: Classifier chains & Margin classifier.read more
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Mutual information based multi-label feature selection via constrained convex optimization
TL;DR: A novel mutual-information-based feature selection method is proposed, which obtains the optimal solution via constrained convex optimization with less time by incorporating the label information into the feature selection process, and label-correlation is taken into consideration to generate the generalized model.
Posted Content
Classifier Chains: A Review and Perspectives
TL;DR: The goal of this work is to provide a review of classifier chains, a survey of the techniques and extensions provided in the literature, as well as perspectives for this approach in the domain of multi-label classification in the future.
Journal ArticleDOI
Cost-sensitive classifier chains: Selecting low-cost features in multi-label classification
TL;DR: An experimental framework in which the features are observed with measurement errors and the costs depend on the quality of the features, which can be recommended in a situation when one wants to balance low costs and high prediction performance.
Journal ArticleDOI
Alignment Based Feature Selection for Multi-label Learning
Linlin Chen,Degang Chen +1 more
TL;DR: A novel method of feature selection for multi-label learning is developed which can learn and address importance degree of labels automatically, and effectiveness of this method is demonstrated by experimental comparisons.
Journal ArticleDOI
Captured multi-label relations via joint deep supervised autoencoder
TL;DR: The deep supervised autoencoder is proposed as a generative model to learn the posterior conditional probability rather than assigning the specific distribution in advance to excavate the real underlying mapping relations hidden in the data sets.
References
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Journal ArticleDOI
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Journal ArticleDOI
Estimating the Dimension of a Model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Estimating the dimension of a model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Journal ArticleDOI
Classification and Regression Trees.
Book
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: In this paper, the authors describe the important ideas in these areas in a common conceptual framework, and the emphasis is on concepts rather than mathematics, with a liberal use of color graphics.