Abstract
The degree of malignancy in brain glioma needs to be assessed by MRI findings and clinical data before operations. There have been previous attempts to solve this problem by using fuzzy max-min neural networks and support vector machines (SVMs), while in this paper, a novel algorithm named PRIFEB is proposed by combining bagging of SVMs with embedded feature selection for its individuals. PRIFEB is compared with the general case of bagging on UCI data sets, experimental results show PRIFEB can obtain better performance than the general case of bagging. Then, PRIFEB is used to predict the degree of malignancy in brain glioma, computation results show that PRIFEB obtains better accuracy than other several methods like bagging of SVMs and single SVMs does.
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Li, GZ., Liu, TY., Cheng, V.S. (2006). Classification of Brain Glioma by Using SVMs Bagging with Feature Selection. In: Li, J., Yang, Q., Tan, AH. (eds) Data Mining for Biomedical Applications. BioDM 2006. Lecture Notes in Computer Science(), vol 3916. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11691730_13
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DOI: https://doi.org/10.1007/11691730_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33104-9
Online ISBN: 978-3-540-33105-6
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