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Improved binary PSO for feature selection using gene expression data
TLDR
Improved binary particle swarm optimization is used in this study to implement feature selection, and the K-nearest neighbor (K-NN) method serves as an evaluator of the IBPSO for gene expression data classification problems. Expand
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Improved binary particle swarm optimization using catfish effect for feature selection
TLDR
The feature selection process constitutes a commonly encountered problem of global combinatorial optimization. Expand
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Chaotic maps based on binary particle swarm optimization for feature selection
TLDR
We propose chaotic binary particle swarm optimization (CBPSO) to implement the feature selection, in which the K-nearest neighbor (K-NN) method with leave-one-out cross-validation serves as a classifier for evaluating classification accuracies. Expand
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Chaotic particle swarm optimization for data clustering
TLDR
Accelerated chaotic particle swarm optimization (ACPSO) searches through arbitrary data sets for appropriate cluster centers and can effectively and efficiently find better solutions. Expand
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IG-GA: A Hybrid Filter/Wrapper Method for Feature Selection of Microarray Data
TLDR
We proposed a filter method (information gain, IG) and a wrapper method (genetic algorithm, GA) for feature selection in microarray data sets. Expand
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A hybrid feature selection method for DNA microarray data
TLDR
In this paper, correlation-based feature selection (CFS) and the Taguchi-genetic algorithm (TGA) were combined into a hybrid method, and the K-nearest neighbor (KNN) with the leave-one-out cross-validation (LOOCV) method served as a classifier for eleven classification profiles to calculate the classification accuracy. Expand
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Boolean binary particle swarm optimization for feature selection
TLDR
We introduce a Boolean function which improves on the disadvantages of standard particle swarm optimization and use it to implement a feature selection for six microarray data sets. Expand
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Gene selection and classification using Taguchi chaotic binary particle swarm optimization
TLDR
The purpose of gene expression analysis is to discriminate between classes of samples, and to predict the relative importance of each gene for sample classification. Expand
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Antioxidant Activity of Various Parts of Cinnamomum cassia Extracted with Different Extraction Methods
The aim of this study was to investigate the antioxidant activities of various parts (barks, buds, and leaves) of Cinnamomum cassia extracted with ethanol and supercritical fluid extraction (SFE).Expand
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Chemical Composition, Antioxidant, and Antibacterial Activity of Wood Vinegar from Litchi chinensis
The antioxidant and antibacterial activities of wood vinegar from Litchi chinensis, and its components have been studied. The chemical compositions of wood vinegar were analyzed by gasExpand
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