[PDF] Top 20 Improved binary PSO for feature selection using gene expression data
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Improved binary PSO for feature selection using gene expression data
... Abstract Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis ...training data sets generally have a fairly small sample ... See full document
5
Feature Selection using PSO-SVM
... Many SVMs have been successfully applied to gene expression data classification problems (Furey et al., 2000; Guyon et al., 2002; Lee et al., 2003) since they are not negatively affected by high ... See full document
3
Feature Selection using PSO-SVM
... Many SVMs have been successfully applied to gene expression data classification problems (Furey et al., 2000; Guyon et al., 2002; Lee et al., 2003) since they are not negatively affected by high ... See full document
3
An Embedded Gene Selection Method for Gene Expression Data
... and binary particle swarm optimization were combined to implement a gene selection process, and K-nearest neighbor (KNN) with Leave-one-out cross validation (LOOCV) method serve as a classifier ... See full document
13
Gene selection and classification using Taguchi chaotic binary particle swarm optimization
... stage feature selection approach: a filter (CFS) and wrapper (TCBPSO) feature selection method were combined in a hybrid method, and KNN with the LOOCV method served as a classifier for ... See full document
5
A hybrid feature selection method for DNA microarray data
... t Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis ...training data sets are generally of a fairly small sample size ... See full document
5
IG-GA: A Hybrid Filter/Wrapper Method for Feature Selection of Microarray Data
... Abstract Gene expression profiles have great potential as a medical diagnostic tool since they represent the state of a cell at the molecular ...training data sets for classification of cancer ... See full document
3
A Hybrid BPSO-CGA Approach for Gene Selection and Classification of Microarray Data
... disease gene com- bination. The classification of gene expression data samples involves feature selection and classifier ...reliable selection method for genes ... See full document
7
A Hybrid Feature Selection Method for Microarray Classification
... Abstract—Gene expression data is widely used in disease analysis and cancer ...since gene expression data could contain thousands of genes simultaneously, successful microarray ... See full document
3
Improved Feature Selection on Microarray Expression Data
... Keywords: Feature Selection, Filter, Wrapper, Support Vector Machine, Microarray ...tool for biologists to discover the gene ...genes expression data could be processed quickly. ... See full document
6
Improved binary particle swarm optimization using catfish effect for feature selection
... The feature selection process constitutes a commonly encountered problem of global combinatorial opti- ...redundant data, thus resulting in acceptable classification accuracy. Feature ... See full document
4
Chaotic maps based on binary particle swarm optimization for feature selection
... 0707. For the identification of relevant features and the removal of irrelevant features two different methods can be employed, namely the filter and wrapper ...the data to evaluate and select feature ... See full document
5
An expert system to classify microarray gene expression data using gene selection by decision tree
... Document type:Journal article (JA) Publisher:Elsevier Ltd, Langford Lane, Kidlington, Oxford, OX5 1GB, United Kingdom Abstract:Gene selection can help the analysis of microarray gene ... See full document
2
A novel feature selection method for large -scale data set
... optimal feature selection method, called bitmap-based feature selection method with discernibility matrix, which employs a discernibility matrix to record the important features during the ... See full document
1
A High-Speed Feature Selection Method For Large Dimensional Data Set
... tool for dealing with data classification ...used for feature reduction and the features that do not contribute towards the classification of the given training data will be ...given ... See full document
33
Comparison of Discrimination Methods for the Classi’ cation of Tumors Using Gene Expression Data
... essential for successful diagnosis and treatment of ...of expression levels in cells for thousands of genes simultaneously, microarray experiments may lead to a more complete understanding of the ... See full document
11
Clustering Complex Data with Group-Dependent Feature Selection
... complex data, we mean that the attribute variations among the data are too extensive such that clustering based on a single feature representation/descriptor is insuffi- cient to faithfully divide ... See full document
14
Classifier design with feature selection and feature extraction using layered genetic programming
... Crossover and mutation are performed on remaining indi- viduals. Offspring are compared with their parents, and the better ones survive to next generation. Only the mutation operator can generate individuals with new ... See full document
10
Data attribute reduction using binary conversion
... Document type:Journal article (JA) Publisher:World Scientific and Engineering Academy and Society, Ag. Ioannou Theologou 17-23, Zographou, Athens, 15773, Greece Abstract:While learning with data having large ... See full document
2
Clustering Gene Expression Time Series Data
... image data and it is scanned and transferred into numeric data which called expression ...The data of several the same Microarray experiments with the same samples under different time points ... See full document
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