An Online Learning Algorithm for a Neuro-Fuzzy Classifier with Mixed-Attribute Data
- Publication Type:
- Journal Article
- Citation:
- 2020
- Issue Date:
- 2020-09-30
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General fuzzy min-max neural network (GFMMNN) is one of the efficient
neuro-fuzzy systems for data classification. However, one of the downsides of
its original learning algorithms is the inability to handle and learn from the
mixed-attribute data. While categorical features encoding methods can be used
with the GFMMNN learning algorithms, they exhibit a lot of shortcomings. Other
approaches proposed in the literature are not suitable for on-line learning as
they require entire training data available in the learning phase. With the
rapid change in the volume and velocity of streaming data in many application
areas, it is increasingly required that the constructed models can learn and
adapt to the continuous data changes in real-time without the need for their
full retraining or access to the historical data. This paper proposes an
extended online learning algorithm for the GFMMNN. The proposed method can
handle the datasets with both continuous and categorical features. The
extensive experiments confirmed superior and stable classification performance
of the proposed approach in comparison to other relevant learning algorithms
for the GFMM model.
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