ieRSPOP: A novel incremental rough set-based pseudo outer-product with ensemble learning

摘要

The rough set-based pseudo outer-product fuzzy neural network (RSPOP FNN) is a member of the pseudo outer-product (POP) FNN family known for high accuracy and interpretability. The POP algorithm utilizes a one-pass rule identification and generation process and rough set theory to perform attribute and rule reduction, hence, producing highly interpretable if-then fuzzy rules while maintaining a high level of accuracy. However, non-incremental systems are heavily dependant on the quality and quantity of the training set, an issue especially prominent in time series data. The robustness of RSPOP FNN is improved using an adapted form of discrete incremental clustering (DIC), an incremental learning algorithm. This renders the system immune to deficiencies in the training set. Issues with the incremental rough set attribute reduction are also addressed using an adapted form of Learn++ Non-Stationary Environments (Learn++.NSE), a form of ensemble learning strong in datasets with the concept drift phenomenon. This is often found in time series data. The proposed system has been extensively benchmarked in traffic flow prediction, real life stock price and volatility predictions. The results show the strength of the online systems against offline systems. The promising results demonstrated the benefit of incremental learning in the accuracy and adaptability of its time series prediction ability. (C) 2016 Published by Elsevier B.V.