Evolving fuzzy classifiers using different model architectures

In this paper we present two novel approaches for on-line evolving fuzzy classifiers, called eClass and FLEXFIS-Class. Both methods can be applied with different model architectures, including single model (SM) with class labels as consequents, classification hyper-planes as consequents, and multi-model (MM) architecture. Additionally, eClass can have a multi-input–multi-output (MIMO) architecture with multiple hyper-planes as consequents of each fuzzy rule. The difference between MM and MIMO architectures is that the former one applies one separate and independent fuzzy rule-based (FRB) classifier for each class and is using an indicator labelling scheme, while the latter one applies a single FRB where the rules are MIMO rather than MISO. Both, eClass and FLEXFIS-Class methods are designed to work on a per-sample basis and are thus one-pass, incremental. Additionally, their structure (FRB) is evolving rather than fixed. It adapts their parameters in antecedent and consequent parts with any newly loaded sample. A special emphasis is placed on advanced issues for improving accuracy and robustness, including a thorough comparison between global and local learning of consequent functions, a novel approach for detecting of and reacting on drifts in the data streams and an enhanced outlier treatment strategy. The methods and their extensions according to the advanced issues are evaluated on one benchmark problem of handwritten images recognition as well as on a real-life problem of image classification framework, where images should be classified into good and bad ones during an on-line and interactive production process.