The classification of dynamical data streams is among the most complex problems encountered in classification. 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. Most Bayesian approaches can be naturally extended to dynamical systems by dynamically tracking the mean of each class using, for example, the exponential moving average based estimator, or a sliding window estimator. The AB schemes introduced by Oommen et al., on the other hand, work with a radically different approach and with the non-central quantiles of the distributions. Surprisingly and counter-intuitively, the reported AB methods work equally or close-to-equally well to an optimal supervised Bayesian scheme. This thus begs its natural extension to the unexplored arena of classification for dynamical data streams. Naturally, for such an AB classification approach, we need to track the non-stationarity of the quantiles of the classes. To achieve this, in this paper, we develop an AB approach for the online classification of data streams by applying the efficient and robust quantile estimators developed by Yazidi and Hammer. Apart from the methodology itself, in this paper, we compare the Bayesian and AB approaches. The results demonstrate the intriguing and counter- intuitive results that the AB approach shows competitive results to the Bayesian approach.
