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An integrity constraint for database systems containing embedded static neural networks
Author(s) -
Millns Iain,
Eaglestone Barry
Publication year - 2001
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/1098-111x(200103)16:3<307::aid-int1009>3.0.co;2-9
Subject(s) - computer science , constraint (computer aided design) , artificial neural network , probabilistic logic , data mining , artificial intelligence , data integrity , database , class (philosophy) , machine learning , mathematics , geometry
Static neural networks are used in some database systems to classify objects, but like traditional statistical classifiers they often misclassify. For some applications, it is necessary to bound the proportion of misclassified objects. This is clearly an integrity problem. We describe a new integrity constraint for database systems with embedded static neural networks, with which a database administrator can enforce a bound on the proportion of misclassifications in a class. The approach is based upon mapping probabilities generated by a probabilistic neural network to the likely percentage of misclassifications. © 2001 John Wiley & Sons, Inc.