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A fuzzified BRAIN algorithm for learning DNF from incomplete data
Aim of this paper is to address the problem of learning Boolean functions
from training data with missing values. We present an extension of the BRAIN
algorithm, called U-BRAIN (Uncertainty-managing Batch Relevance-based
Artificial INtelligence), conceived for learning DNF Boolean formulas from
partial truth tables, possibly with uncertain values or missing bits.
Such an algorithm is obtained from BRAIN by introducing fuzzy sets in order
to manage uncertainty. In the case where no missing bits are present, the
algorithm reduces to the original BRAIN