Concise, intelligible, and approximate profiling of multiple classes

نویسندگان

  • Raúl E. Valdés-Pérez
  • Francisco Pereira
  • Vladimir Pericliev
چکیده

When a dataset involves multiple classes, there is often a need to express the key contrasting features among these classes in humanly understandable terms, that is, to pro"le the classes. Commonly, one class is contrasted from the rest by aggregating the latter into a pseudo-class; alternatively, classes are treated separately without coordinating their pro"les with those of the other classes. We introduce the concise all pairs pro,ling (CAPP) method for concise, intelligible, and approximate pro"ling of large classi"cations. The method compares all classes pairwise and then minimizes the overall number of features needed to guarantee that each pair of classes is contrasted by at least one feature. Then each class pro"le gets its own minimized list of features, annotated with how these features contrast the class from the others. Signi"cant applications to social and natural science are demonstrated. ( 2000 Academic Press

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عنوان ژورنال:
  • Int. J. Hum.-Comput. Stud.

دوره 53  شماره 

صفحات  -

تاریخ انتشار 2000