Abstract
The purpose of this paper is to develop an efficient approach to improve medical diagnosis performance of breast cancer. First, the medical dataset of breast cancer is selected from UCI Machine Learning Repository. After that, the standardization and normalization of datasets are pre-processing procedure. Secondly, the proposed approach combines support vector machine with artificial immune system as the medical diagnosis classifier. The results of diagnosis are identified and the rates of classification accuracy are evaluated. A simple artificial immune algorithm with various affinity criteria is investigated for comparison. Furthermore, the grid-search with 10-fold cross-validation is applied to choose two parameters of C and γ for AIS-based machine learning classifier. Through grid-search technique, the proposed classifier could yield the best results.
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Cheng, HP., Lin, ZS., Hsiao, HF., Tseng, ML. (2010). Designing an Artificial Immune System-Based Machine Learning Classifier for Medical Diagnosis. In: Zhu, R., Zhang, Y., Liu, B., Liu, C. (eds) Information Computing and Applications. ICICA 2010. Lecture Notes in Computer Science, vol 6377. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16167-4_43
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DOI: https://doi.org/10.1007/978-3-642-16167-4_43
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