Feature extraction for breast cancer data based on geometric algebra theory and feature selection using differential evolution
Li, J.; Hong, W.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 31(6): 1218-22 1228
2014
ISSN/ISBN: 1001-5515 PMID: 25868233 Document Number: 675575
The feature extraction and feature selection are the important issues in pattern recognition. Based on the geometric algebra representation of vector, a new feature extraction method using blade coefficient of geometric algebra was proposed in this study. At the same time, an improved differential evolution (DE) feature selection method was proposed to solve the elevated high dimension issue. The simple linear discriminant analysis was used as the classifier. The result of the 10-fold cross-validation (10 CV) classification of public breast cancer biomedical dataset was more than 96% and proved superior to that of the original features and traditional feature extraction method.