Journal Article10.1016/J.COMPBIOMED.2015.04.011
Improving PLS-RFE based gene selection for microarray data classification
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TL;DR: Experimental results demonstrate that the two proposed approaches accelerate the feature selection process impressively without degrading the classification accuracy and obtain more compact feature subsets for both two-category and multi-category problems.
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About: This article is published in Computers in Biology and Medicine. The article was published on 01 Jul 2015. The article focuses on the topics: Feature selection & Naive Bayes classifier.
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Citations
A hybrid gene selection algorithm for microarray cancer classification using genetic algorithm and learning automata
TL;DR: A hybrid meta-heuristic algorithm, which is an integration of Genetic Algorithm and Learning Automata (GALA), is proposed for gene selection in cancer classification and it has acceptable accuracy and performance on some well-known cancer datasets.
114
An efficient feature selection framework based on information theory for high dimensional data
G. Manikandan,S. Abirami +1 more
TL;DR: An effective feature selection technique called mutual information and Monte Carlo based feature selection (MIMCFS) is proposed in order to completely eradicate redundant features and to improve feature interaction.
40
Clustering-based hybrid feature selection approach for high dimensional microarray data
TL;DR: In this article, a clustering-based hybrid gene selection approach was proposed to reduce the high dimensionality and increase the classification accuracy of cancer microarray data, which used the combined method of k-means clustering algorithm and signal-to-noise-ratio ranking method as a primary filtering method.
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An efficient model selection for linear discriminant function-based recursive feature elimination
Xiaoli Ding,Fan Yang,Fuming Ma +2 more
TL;DR: In this paper , an approximation method was proposed to evaluate the generalization error of a linear SVM-RFE, and a new criterion was designed to tune the penalty parameter C. The performance of the proposed algorithm exceeds that of the compared algorithms on bioinformatics datasets, and empirically demonstrate the computational time saving achieved by alpha seeding approaches.
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A metaheuristic optimization framework for informative gene selection
TL;DR: A metaheuristic framework using Harmony Search (HS) with Genetic Algorithm (GA) for gene selection with impressive accuracy over existing feature selection approaches is presented.
22
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Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.
Todd R. Golub,Todd R. Golub,Donna K. Slonim,Pablo Tamayo,Christine Huard,Michelle Gaasenbeek,Jill P. Mesirov,Hilary A. Coller,Mignon L. Loh,James R. Downing,Michael A. Caligiuri,Clara D. Bloomfield,Eric S. Lander +12 more
TL;DR: A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case and suggests a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
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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Gene Selection for Cancer Classification using Support Vector Machines
TL;DR: In this article, a Support Vector Machine (SVM) method based on recursive feature elimination (RFE) was proposed to select a small subset of genes from broad patterns of gene expression data, recorded on DNA micro-arrays.