Evolving clustering, classification and regression with TEDA

Dmitry Kangin, Plamen Angelov
2015 2015 International Joint Conference on Neural Networks (IJCNN)  
In th i s art i cle the novel cluster i ng and regress i on methods TEDACluster and TEDAPredict methods are descr i bed additionally to recently proposed evolv i ng class i fier TEDAClass. The algor i thms for class i fication, cluster i ng and regression are based on the recently proposed AnYa type fuzzy rule based system. The novel methods use the recently proposed TEDA framework capable of recursive process i ng of large amounts of data. The framework i s capable of computationally cheap
more » ... ationally cheap exact update of data per sample, and can be used for training 'from scratch'. All three algor i thms are evolv i ng that is they are capable of chang i ng i ts own structure dur i ng the update stage, wh i ch allows to follow the changes with i n the model pattern.
doi:10.1109/ijcnn.2015.7280528 dblp:conf/ijcnn/KanginA15 fatcat:by7zsla2krdqxjs6j2xbqudi2u