A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression
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Abstract
This paper studies the problem of building multiclass classifiers for tissue classification based on gene expression. The recent development of microarray technologies has enabled biologists to quantify gene expression of tens of thousands of genes in a single experiment. Biologists have begun collecting gene expression for a large number of samples. One of the urgent issues in the use of microarray data is to develop methods for characterizing samples based on their gene expression. The most basic step in the research direction is binary sample classification, which has been studied extensively over the past few years. This paper investigates the next step-multiclass classification of samples based on gene…
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Authors
3Topics & keywords
Topics
Keywords
- Feature selection
- Multiclass classification
- Computer science
- Artificial intelligence
- Expression (computer science)
- Selection (genetic algorithm)
- Sample (material)
- Binary classification
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