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 Relevancebased
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.
functions from training data with missing values. We present an extension of the
BRAIN algorithm, called U-BRAIN (Uncertainty-managing Batch Relevancebased
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.
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Salvatore Rampone
Data:
2012