Journal Article10.1016/j.asoc.2023.110102
A multi-objective evolutionary algorithm with decomposition and the information feedback for high-dimensional medical data
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TL;DR: In this article , a multi-objective evolutionary algorithm that integrates decomposition and the information feedback model (IFMMOEAD) is proposed for high-dimensional medical data, which not only considers the number of selected features, but also classification accuracy and correlation measures of features when feature dimensionality reduction is executed.
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About: This article is published in Applied Soft Computing. The article was published on 01 Feb 2023. The article focuses on the topics: Computer science & Computer science.
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References
An introduction to variable and feature selection
Isabelle Guyon,André Elisseeff +1 more
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
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Wrappers for feature subset selection
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TL;DR: The wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain and compares the wrapper approach to induction without feature subset selection and to Relief, a filter approach tofeature subset selection.
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MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Qingfu Zhang,Hui Li +1 more
TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.
Feature selection based on mutual information: criteria ofmax-dependency, max-relevance, and min-redundancy
Hanchuan Peng,Fuhui Long,Chris Ding +2 more
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TL;DR: This work derives an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order incremental feature selection, and presents a two-stage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers).
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