Data preprocessing, such as normalization, feature extraction, and dimension reduction, is necessary to better accomplish the classification of data. The aim of preprocessing is to find the most informative set of features to improve the performance of the classifier. Thresholding converts an ordinal or a quantitative feature into a Boolean feature by finding a feature value to divide. Feature normalization is mostly needed to eliminate the effect of several quantitative features measured on different scales. Furthermore, feature extraction is utilized to extract features from the raw data to achieve a consistent classification. Feature extraction is the most critical part of the signal classification since ...
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