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A fuzzy binary neural network for interpretable classifications
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DOI：10.1016/j.neucom.2013.05.030.png)
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Abstract
En 中文
Classification is probably the most frequently encountered problem in machine learning (ML). 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, a support vector machine, an adaptive neuro-fuzzy inference system, and fuzzy and standard K-nearest neighbour classification. Besides its advantage of being interpretable, the proposed system shows strong performance on most of the data sets. (C) 2013 Elsevier B.V. All rights reserved.
Keywords:
Neural networks
Machine learning
Supervised classification
Fuzzy reasoning
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