A fuzzified BRAIN algorithm for learning DNF from incomplete data

TL;DR

This paper introduces U-BRAIN, a fuzzy extension of the BRAIN algorithm, designed to learn DNF Boolean formulas from incomplete and uncertain data, improving robustness in data with missing values.

Contribution

The paper presents U-BRAIN, a novel fuzzy-based extension of BRAIN, capable of learning DNF formulas from incomplete datasets with uncertain or missing values.

Findings

U-BRAIN effectively handles missing data in Boolean function learning.

The algorithm reduces to BRAIN when no missing bits are present.

U-BRAIN demonstrates robustness in uncertain data scenarios.

Abstract

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.