Empirical Comparisons of Online Boosting Algorithms
Xiaowei Sun · Advances in computer science research · 2015
Boosting is an effective classifier combination method, which can improve classification performance of an unstable learning algorithm due to its theoretical performance guarantees and strong experimental results.However, the algorithm has been used mainly in batch mode, i.e., it requires the entire training set to be available at once and, in some cases, require random access to the data.Recently, Nikunj C.oza(2001) proved that some preliminary theoretical results and some empirical comparisons of the classification accuracies of online algorithms with their corresponding batch algorithms on many datasets.In this paper, we present online versions of some boosting methods that require only one pass through the training data.Specifically, we discuss how our online algorithms mirror the techniques that boosting use to generate multiple distinct base models.We also present theoretical and experimental evidence that our online algorithms succeed in this mirroring.Our online algorithms are demonstrated to be more practical with larger datasets.We also compare the online and batch algorithms experimentally in terms of accuracy .