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In machine learning, feature selection is the process of selecting a subset of relevant features for use in model construction. A comparative evaluation between selection methods: SNR, Correlation Coefficient and Max-relevance Min-Redundancy is carried out, using the dataset of prostate cancer. The Evaluation of the dimensionality reduction was done by using the supervised classifier K Nearest Neighbors...
This paper presents a study of feature selection methods effect, using a filter approach, on the accuracy and error of supervised classification of cancer. A comparative evaluation between different selection methods: Fisher, T-Statistics, SNR and ReliefF, is carried out, using the dataset of different cancers; leukemia cancer, prostate cancer and colon cancer. The classification results using k nearest...
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