Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework
By Zexuan Zhu, Yew-Soon Ong, Manoranjan Dash. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2007
469 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/9fe9d1f47d460a5072c54769a40de2a5b4143bca
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Citations in the corpus
- An Effective Feature Selection Method via Mutual Information Estimation (2012, background): “Methods for feature selection are typically classified as filter or wrapper [1], [2], [4], [5].”
- A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue (2007, background): “Feature selection aims at finding out a powerfully predictive subset of features within a database and try best to reduce the number of features presented to the modeling process [25—28].”
- Feature selection for high-dimensional classification using a competitive swarm optimizer (2016, methodology): “For example, Zhu et al. (2007) have proposed to use a GA combined with local search in a hybrid wrapper and filter feature selection algorithm.”
- Markov blanket-embedded genetic algorithm for gene selection (2007, background): “One way to solve this problem is to hybridize GA with some memetic operations (also known as local search operations) [30] [31] [32] [33] [34] which are capable of fine-tuning and improving the solutions generated by the GA to make them more accurate and efficient.”
- Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning (2020, unknown): “and reduce computational complexity [76].”
- Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms (2014, methodology): “[16] propose a feature selection method using a memetic algorithm that is a combination of local search and GA.”
- Neighborhood rough set based heterogeneous feature subset selection (2008, background): “There are several viewpoints to categorize such techniques: filter, wrapper and embedded [21,41], unsupervised [20] and supervised [1,11–14,22], etc.”
- Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach (2013, unknown): “[26] propose a hybrid wrapper and filter feature”