Evolving Fuzzy-Rule-Based Classifiers From Data Streams

A new approach to the online classification of streaming data is introduced in this paper. It is based on a self-developing (
e
volving) fuzzy-rule-based (FRB) classifier system of
T
akagi-
S
ugeno (
eTS
) type. The proposed approach, called
eClass
(
e
volving
class
ifier), includes different architectures and online learning methods. The family of alternative architectures includes: 1)
eClass0
, with the classifier consequents representing class label and 2) the newly proposed method for regression over the features using a first-order
eTS
fuzzy classifier,
eClass1
. An important property of
eClass
is that it can start learning ldquofrom scratch.rdquo Not only do the fuzzy rules not need to be prespecified, but neither do the number of classes for
eClass
(the number may grow, with new class labels being added by the online learning process). In the event that an initial FRB exists,
eClass
can evolve/develop it further based on the newly arrived data. The proposed approach addresses the practical problems of the classification of streaming data (video, speech, sensory data generated from robotic, advanced industrial applications, financial and retail chain transactions, intruder detection, etc.). It has been successfully tested on a number of benchmark problems as well as on data from an intrusion detection data stream to produce a comparison with the established approaches. The results demonstrate that a flexible (with evolving structure) FRB classifier can be generated online from streaming data achieving high classification rates and using limited computational resources.