Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification
By Jianhua Dai, Qing Xu. Applied Soft Computing, 2013
345 citations recorded by the source corpus.
Tumor classification based on gene expression levels is important for tumor diagnosis. Since tumor data in gene expression contain thousands of attributes, attribute selection for tumor data in gene expression becomes a key point for tumor classification. Inspired by the concept of gain ratio in decision tree theory, an attribute selection method based on fuzzy gain ratio under the framework of fuzzy rough set theory is proposed. The approach is compared to several other approaches on three real world tumor data sets in gene expression. Results show that the proposed method is effective. This work may supply an optional strategy for dealing with tumor data in gene expression or other applications.
Subfields: Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Information Systems
View Paper: https://www.semanticscholar.org/paper/272789a8e2710f61ab2cd6ce565e3793db814a62
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In Computational Theory and Mathematics
In Computer Vision and Pattern Recognition
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Citations in the corpus
- Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods (2017, methodology): “Several feature selection methods used in the literature review such us information gain ratio (Dai and Xu 2013), consistency (Dash and Liu 2003), gain ratio (Karegowda et al.”