An Evolving Cascade System Based on A Set Of Neo Fuzzy Nodes
This work addresses the need for efficient and adaptive systems in data mining, particularly for time series forecasting, but appears incremental as it builds on existing neo-fuzzy concepts.
The paper tackles the problem of online tuning of both parameters and architecture in data mining tasks, specifically time series forecasting, by proposing an evolving cascade system based on neo-fuzzy nodes, achieving high speed and effectiveness in processing large datasets.
Neo-fuzzy elements are used as nodes for an evolving cascade system. The proposed system can tune both its parameters and architecture in an online mode. It can be used for solving a wide range of Data Mining tasks (namely time series forecasting). The evolving cascade system with neo-fuzzy nodes can process rather large data sets with high speed and effectiveness.