Pareto front feature selection based on artificial bee colony optimization

About this item

Title

Pareto front feature selection based on artificial bee colony optimization

Content partner

Victoria University of Wellington

Collection

Open Access Victoria University of Wellington

Description

© 2017 Elsevier Inc. Feature selection has two major conflicting aims, i.e., to maximize the classification performance and to minimize the number of selected features to overcome the curse of dimensionality. To balance their trade-off, feature selection can be handled as a multi-objective problem. In this paper, a feature selection approach is proposed based on a new multi-objective artificial bee colony algorithm integrated with non-dominated sorting procedure and genetic operators. Two dif...

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Format

Research paper

Research format

Scholarly text / Journal article

Date created

2018

Creator

E Hancer / Bing Xue / Mengjie Zhang / D Karaboga / B Akay

URL

https://figshare.com/articles/journal_contribution/Pareto_front_feature_selection_based_on_artifi...

Related subjects

Artificial intelligence not elsewhere classified / Science & Technology / Technology / Computer Science, Information Systems / Computer Science / Feature selection / Classification / Multi-objective optimization / Artificial bee colony / PARTICLE SWARM OPTIMIZATION / EVOLUTIONARY ALGORITHM / MUTUAL INFORMATION / CLASSIFICATION / Artificial Intelligence & Image Processing / Mathematical Sciences / Information and Computing Sciences / Engineering / Artificial Intelligence and Image Processing

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