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A Fuzzy Binary Neural Network for Interpretable Classifications

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JournalNeurocomputing
DateE-pub ahead of print - 18 Jun 2013
DatePublished (current) - 9 Dec 2013
Volume121
Number of pages415
Pages (from-to)401
Early online date18/06/13
Original languageEnglish

Abstract

Classification is probably the most frequently encountered problem in
machine learning. The most successful ML techniques like multi-layer
perceptrons or support vector machines constitute very complex systems
and the underlying reasoning processes of a classification decision
are most often incomprehensible. We propose a classification system
based on a hybridization of binary correlation matrix memories and
fuzzy logic that yields interpretable solutions to classification
tasks. A binary correlation matrix memory is a simple single-layered
network consisting of a matrix with binary weights with easy to
understand dynamics. Fuzzy logic has proven to be a suitable framework
for reasoning under uncertainty and modelling human language concepts.
The usage of binary correlation matrix memories and of fuzzy logic
facilitates interpretability. Two fuzzy recall algorithms carry out
the classification. The first one resembles fuzzy inference, uses
fuzzy operators and can directly be translated into a fuzzy ruleset in
human language. The second recall algorithm is based on a well known
classification technique, that is fuzzy k-nearest neighbour
classification. The proposed classifier is benchmarked on six
different data sets and compared to other systems, that is, a
multi-layer perceptron and fuzzy and standard k-nearest neighbour
classification. Besides its advantage of being interpretable, the
proposed system is also able to outperform the other classifiers on
most of the data sets.

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