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THE RELATION BETWEEN ORDINAL PROBLEM SPACE SIZES AND THE MAXIMUM NUMBER OF ORDINAL CLASSIFICATION RULES
Author(s) -
BENDAVID ARIE
Publication year - 1993
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.1993.tb00299.x
Subject(s) - mathematics , relation (database) , a priori and a posteriori , function (biology) , space (punctuation) , ordinal regression , class (philosophy) , selection (genetic algorithm) , ordinal optimization , artificial intelligence , computer science , machine learning , statistics , data mining , philosophy , epistemology , evolutionary biology , biology , operating system
A method is presented of establishing bounds on the number of classification rules in such applications as credit worthiness assessment, investment decisions, premium determination, consumer choices, employee selection, and editorial preferences, to name just a few. A function that relates the maximum number of classification rules to the problem space size of such application domains is established. It is shown that in this important class of ordinal classification problems, the maximum possible number of rules is significantly lower than the relative problem space sizes. The approach grants the ability to a priori estimate worst case response time and memory requirements, and to better predict the effectiveness of knowledge acquisition efforts.