The classification of dynamical data streams is among the most complex problems encountered in classification. This is, firstly, because the distribution of the data streams is non-stationary, and it changes without any prior 'warning'. Secondly, the manner in which it changes is also unknown. Thirdly, and more interestingly, the model operates with the assumption that the correct classes of previously-classified patterns become available at a juncture after their appearance. This paper pioneers the use of unreported novel schemes that can classify such dynamical data streams by invoking the recently-introduced 'Anti-Bayesian' (AB) techniques. Contrary to the Bayesian paradigm, that compare the testing sample with the distribution's central points, AB techniques are based on the information in the distant-from-the-mean samples.

Additional Metadata
Keywords Anti-Bayesian Classification, Classification With delay, Data Streams, Incremental Quantile Estimation
Persistent URL dx.doi.org/10.1109/CEC.2017.7969439
Conference 2017 IEEE Congress on Evolutionary Computation, CEC 2017
Citation
Hammer, H.L. (Hugo Lewi), Yazidi, A. (Anis), & Oommen, J. (2017). On using novel 'Anti-Bayesian' techniques for the classification of dynamical data streams. In 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings (pp. 1173–1182). doi:10.1109/CEC.2017.7969439