Pattern Recognition Letters
Journal of Mathematical Psychology
Modern mathematical methods for physicists and engineers
Modern mathematical methods for physicists and engineers
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
An overview of anomaly detection techniques: Existing solutions and latest technological trends
Computer Networks: The International Journal of Computer and Telecommunications Networking
Self-estimation of Data and Approximation Reliability through Neural Networks
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Behavior-Based Credit Card Fraud Detecting Model
NCM '09 Proceedings of the 2009 Fifth International Joint Conference on INC, IMS and IDC
Local area network anomaly detection using association rules mining
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
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Data preprocessing is a main step in data mining because real data can be corrupted for different causes and high performance data mining systems require high quality data. When a database is used for training a neural network, a fuzzy system or a neuro-fuzzy system, a suitable data selection and pre-processing stage can be very useful in order to obtain a reliable result. For instance, when the final aim of a system trained through a supervised learning procedure is to approximate an existing functional relationship between input and output variables, the database that is exploited in the system training phase should not contain input-output patterns for which the same input or similar input sets are associated to very different values of the output variable. In this paper a procedure is proposed for detecting non-coherent associations between input and output patterns: by comparing two distance matrices associated to the input and output patterns, the elements of the available dataset, where similar values of input variables are associated to quite different output values can be pointed out. The efficiency of the proposed algorithm when pre-processing data coming from an industrial database is presented and discussed together with a statistical assessment of the obtained results.