This paper presents a learning algorithm for fuzzy neural networks based on unineurons able to generate interpretation provided by the model through fuzzy rules. The learning algorithm is based on ideas from Extreme Learning Machine, to achieve a low time complexity, and pruning method based on F-scores resulting in accurate models using low complexity resources, using only training data in a single step. Experiments considering binary pattern classification are detailed. Results and statistical evaluation suggest the suggested approach as a promising alternative for pattern recognition with a good accuracy and some level of interpretability through a process of pruning performed in simple steps.
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