An Online Learning Algorithm for a Neuro-Fuzzy Classifier with Mixed-Attribute Data
This work addresses the need for real-time adaptation to streaming data with mixed attributes, which is incremental as it extends an existing neuro-fuzzy system.
The paper tackled the inability of existing General Fuzzy Min-Max Neural Network (GFMMNN) learning algorithms to handle mixed-attribute data in online settings, proposing an extended online learning algorithm that achieved superior and stable classification performance compared to other relevant methods.
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.