Abstract
General type-2 fuzzy systems have been shown to handle more levels of uncertainty present in the majority of real-world applications. Nevertheless, the rapid growth of information generation does not allow utilizing general type-2 models for their complex learning process. This paper introduces a novel online general type-2 fuzzy classifier (called oGT2FC). It aims to reduce the computations needed to get the type-2 fuzzy sets. As in most online and evolving fuzzy schemes, the initial rule-base in oGT2FC is empty, and then the fuzzy rules are generated in completely online manner; without storing the samples. To specify the type-2 fuzzy sets, oGT2FC employs some experts' opinions, drawn from training data, to generate automatically some diverse type-1 fuzzy rule-bases. These type-1 rule-bases are updated/evolved by incoming new samples and are used to construct the general type-2 model. By defining a type-2 fuzzy set as the union of vertical slices, oGT2FC performs the type reduction in a fast and efficient manner. The efficiency of the proposed oGT2FC is assessed experimentally, using synthetic and real-world data streams, via comparing with other type-2 and type-1 evolving fuzzy classifiers as well as some state-of-the-art incremental algorithms. In addition, oGT2FC is compared against some fuzzy classifiers in its ability to model uncertainty.
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