Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Classification of Storm Events Using a Fuzzy Encoded Multilayer Perceptron
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Applied Soft Computing
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
Fuzzy quartile encoding as a preprocessing method for biomedical pattern classification
Theoretical Computer Science
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A fuzzy set theoretic methodology is described that serves as a classification preprocessing strategy for supervised feed-forward neural networks. This methodology, fuzzy interquartile encoding, determines the respective degrees to which a feature belongs to a collection of fuzzy sets that overlap at the respective quartile boundaries of the feature. These membership values are subsequently used in place of the original feature. This transformation has a normalizing effect on the feature space and is more robust to feature outliers. Its effectiveness is scrutinized using several synthetic data sets with various underlying distributions. Fuzzy interquartile encoding is shown to consistently improve the discriminatory power of the underlying classifiers. The methodology is also applied to two biomedical data sets relating to tonsillectomy and/or adenoidectomy patients who may or may not have had a predisposition to excessive bleeding during their operation. The features of the first data set are blood sample test results acquired from a coagulation laboratory and the class labels are one of three hemostatic defects as identified by the reference tests. The second data set consists of patient responses to queries from a bleeding tendency questionnaire. Normal and abnormal class labels were derived from a hematology expert system designed in consultation with a pediatric hematologist. Fuzzy interquartile encoding effected an 11% improvement in the classification accuracy of the underlying neural network classifier with the former data set and 18% with the latter.