ArticleBMC medical genomics2016
Gene masking - a technique to improve accuracy for cancer classification with high dimensionality in microarray data.
Article in BMC medical genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A transcriptomic study for identifying cardia- and non-cardia-specific gastric cancer prognostic factors using genetic algorithm-based methods.Journal of cellular and molecular medicine · 2020Article
- 2016 update on APBioNet's annual international conference on bioinformatics (InCoB).BMC genomics · 2016Article
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Authors and funding
7 authors.
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Abstract
backgroundHigh dimensional feature space generally degrades classification in several applications. In this paper, we propose a strategy called gene masking, in which non-contributing dimensions are heuristically removed from the data to improve classification accuracy.
methodsGene masking is implemented via a binary encoded genetic algorithm that can be integrated seamlessly with classifiers during the training phase of classification to perform feature selection. It can also be used to discriminate between features that contribute most to the classification, thereby, allowing researchers to isolate features that may have special significance.
resultsThis technique was applied on publicly available datasets whereby it substantially reduced the number of features used for classification while maintaining high accuracies.
conclusionThe proposed technique can be extremely useful in feature selection as it heuristically removes non-contributing features to improve the performance of classifiers.
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