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Guest editorial: new trends in data pre-processing methods for signal and image classification
(Springer, 2017) Polat, Kemal; Muthusamy, Hariharan; Acharya, Rajendra; Guo, Yanhui
A special issue of the Neural Computing and Applications (NCAA) is dedicated to ‘‘New trends in data pre-processing methods for signal and image classification.’’Data pre-processing is crucial for effective data mining. Low-quality data usually produce inaccurate and unpredictable outcomes. Today’s real-world data are greatly vulnerable to noise and getting lost due to either large data size or the sources of origin. Real-world data are often inconsistent and incomplete, and are possible to have several errors. These poor-quality data will result in poorquality mining outcomes. Data pre-processing enhances the data standard and subsequently aids to refine the value of data mining outcomes. Data pre-processing performs certain processing on raw original data to prepare it for further processing or analysis. In short, data pre-processing prepares original raw data for further processing. Data preprocessing converts the data into a form acceptable easily for further processing by the user.