Method for finding a best test for a nominal attribute for gener

Image analysis – Pattern recognition – Classification

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382227, 382240, G06K 936, G06K 946, G06K 968

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active

061012750

ABSTRACT:
A fast way for determining the best subset test for a nominal attribute in a decision tree. When a nominal attribute has n distinct values, the prior art requires computing the impurity functions on each of the 2.sup.n-1 -1 possible subset partitioning of the n values and finding the minimum case among them. This invention guarantees the minimum impurity test on the attribute by computing only (n-1) impurity function computations. This reduction of computational complexity makes it practically possible to find the true best tests for many real data mining application, where a binary decision tree is used as the classification model.

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Yoshikawa, et al "A Fully Automated Design of Binary Decision Tree for Land Cover Classification", IEEE, pp. 1921-1923, Feb. 1995.
Aborhey "Binary Decision Tree Test Functions", IEEE, pp. 1461-1465, 1988.

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