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In Chappaqua, one of the county's wealthiest districts, the classification rate has been traditionally low.
The overall correct classification rate obtained was 90.6%.
The method demonstrates the achievements of promising classification rate.
As a result, existing methods cannot assure constant classification rate.
Classification rate has been calculated over the different combination of channels.
Correct classification rate of GRNN using all the extracted features as an input vector is 97.5%.
A significant gain in the global classification rate can be obtained by using an NNIG.
However, the TOMM achieved a higher overall classification rate than the Dot Counting Test.
Hence, the classification rate is constant regardless of the ruleset properties.
The second phase develops the geometry of hyperboxes to improve the classification rate.
As result, the highest classification rate as high as 94.7% is achieved in session During.
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