On the Minimization of Variables to Represent Partially Defined Classification Functions
Tsutomu Sasao · 2020
A partially defined classification function is a mapping from the set of k distinct vectors of n bite to m elements, wherekn. Such a function can often be represented with fewer variables than n, by appropriately assigning valus to don't cares. The number of variables can be further reduced by a linear transformation of the input variables. This paper shows an efficient method to find a linear transformation that reduces the number of variables. The method is illustrated with examples.