Cascading Randomized Weighted Majority: A New Online Ensemble Learning Algorithm

With the increasing volume of data in the world, the best approach for learning from this data is to exploit an online learning algorithm. Online ensemble methods are online algorithms which take advantage of an ensemble of classifiers to predict labels of data. Prediction with expert advice is a well-studied problem in the online ensemble learning literature. The Weighted Majority algorithm and the randomized weighted majority (RWM) are the most well-known solutions to this problem, aiming to converge to the best expert. Since among some expert, the best one does not necessarily have the minimum error in all regions of data space, defining specific regions and converging to the best expert in each of these regions will lead to a better result. In this paper, we aim to resolve this defect of RWM algorithms by proposing a novel online ensemble algorithm to the problem of prediction with expert advice. We propose a cascading version of RWM to achieve not only better experimental results but also a better error bound for sufficiently large datasets.Comment: 15 pages, 3 figure

Возможности применения методов интеллектуального анализа данных в системах обнаружения вторжений

Possibility application of Data Mining methods in the intrusion detection systems is presented.Представлена возможность применения методов интеллектуального анализа данных в системах обнаружения атак

Реалізація повних підстановок за допомогою багатомодульного каскаду найпростіших конструктивних модулів

Досліджується можливість реалізації масових повних підстановок за допомогою найпростішого одновимірного каскаду конструктивних модулів.A possibility of large-scal complete permutations realization with the simplest one-dimensional cascade of constructive modules is considered

Ensemble-based adaptive intrusion detection

of motion fields under spatio-temporal non-uniform illuminatio