Journal ArticleDOI
Cost-sensitive classifier chains: Selecting low-cost features in multi-label classification
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TLDR
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.About:
This article is published in Pattern Recognition.The article was published on 2019-02-01. It has received 20 citations till now. The article focuses on the topics: Classifier chains & Multi-label classification.read more
Citations
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Journal ArticleDOI
The monarch butterfly optimization algorithm for solving feature selection problems
Mohammed Alweshah,Saleh Al khalaileh,Brij B. Gupta,Brij B. Gupta,Ammar Almomani,Abdelaziz I. Hammouri,Mohammed Azmi Al-Betar +6 more
TL;DR: The use of the MBO to solve the FS problems has been proven through the results obtained to be effective and highly efficient in this field, and the results have also proven the strength of the balance between global and local search of MBO.
Posted Content
Bayesian Network Based Label Correlation Analysis For Multi-label Classifier Chain
TL;DR: This paper employs Bayesian network (BN) to model the label correlations and proposes a new BN-based CC method (BNCC), which derives the label order for constructing CC model by applying topological sorting on the nodes of the optimized BN.
Journal ArticleDOI
Bayesian network based label correlation analysis for multi-label classifier chain
TL;DR: In this paper, a new BN-based CC method (BNCC) is proposed, where the conditional entropy is used to describe the dependency relations among labels, and a BN is built up by taking nodes as labels and weights of edges as their dependency relations.
Journal ArticleDOI
Joint feature extraction and classification in a unified framework for cost-sensitive face recognition
Jianwu Wan,Yinjuan Chen,Bing Bai +2 more
TL;DR: Wang et al. as mentioned in this paper proposed to incorporate feature extraction and classification in a unified cost-sensitive framework for face recognition, which can significantly reduce the overall misclassification loss of face recognition system as well as the classification errors associated with high costs.
References
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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.
Journal ArticleDOI
Regularization and variable selection via the elastic net
Hui Zou,Trevor Hastie +1 more
TL;DR: It is shown that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation, and an algorithm called LARS‐EN is proposed for computing elastic net regularization paths efficiently, much like algorithm LARS does for the lamba.
Journal ArticleDOI
Regularization Paths for Generalized Linear Models via Coordinate Descent
TL;DR: In comparative timings, the new algorithms are considerably faster than competing methods and can handle large problems and can also deal efficiently with sparse features.
Journal Article
Statistical Comparisons of Classifiers over Multiple Data Sets
TL;DR: A set of simple, yet safe and robust non-parametric tests for statistical comparisons of classifiers is recommended: the Wilcoxon signed ranks test for comparison of two classifiers and the Friedman test with the corresponding post-hoc tests for comparisons of more classifiers over multiple data sets.