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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. Data pre-processing methods have various applications in signal and image processing such as signal and image pre-processing, feature extraction, feature dimension reduction, classification and idea or information extraction. Data pre-processing can be applied to (1) remove noise part from signal or image, otherwise increase the consequence of reaching incorrect conclusions using raw signal or image data, (2) extract features, reduce the dimensionality of the signal or image and maintain as much significant information as possible, and (3) develop concept formation from the signal or image data. The main focus of this issue is the application of soft computing on signal and image classification using different and new data pre-processing methods on the following problems: signal detection, image detection, personal identification systems, iris recognition, face recognition, biomedical signal and image classification. Totally 57 papers were submitted and sent out for peer review. The peer review process was conducted according to the standing editorial policy of Neural Computing and Applications which results in the final versions of the 20 papers accepted and included in this special issue. A summary of the papers appears below. R. Alejo et al. presented an improved dynamic sampling approach (ISDSA) for facing the multi-class imbalance problem. ISDSA uses the mean square error (MSE) and a Gaussian function to identify the best samples to train the neural network. Results show that ISDSA makes better exploitation of the training dataset, improves the MLP classification performance, and deals the multi-class imbalance problem successfully. In addition, results indicate that the proposed method is very competitive in terms & Kemal Polat kemal_polat2003@yahoo.com

Guest editorial: New trends in data pre-processing methods for signal and image classification