Title :
Controlling selectivity in nonstandard pattern recognition algorithms
Author :
Carreté, Nuria Piera ; Aguilar-Martin, Joseph
Author_Institution :
CNRS, Toulouse, France
Abstract :
A type of aggregation operator, referred to as mixed connectives, is used to summarize the information about objects to be classified (supplied by the descriptors). Because mixed connectives depend on a parameter, this leads to the concept of families for these operators. Therefore, given such a family, it is possible to associate different classifications with the same data set, depending on the value chosen for the parameter. The manner in which the algorithm selectivity classifications are compared is illustrated by an example involving a quantitative data basis
Keywords :
aggregation; pattern recognition; aggregation operator; classifications; mixed connectives; nonstandard pattern recognition algorithms; quantitative data basis; selectivity; Chaos; Classification algorithms; Clustering algorithms; Context modeling; Data analysis; Data mining; Entropy; Machine learning; Pattern analysis; Pattern recognition;
Journal_Title :
Systems, Man and Cybernetics, IEEE Transactions on