Title: An Ensemble Classification Algorithm Based on Information Entropy for Data Streams,
Abstract: Data stream mining problem has caused widely concerns in the area of machine
learning and data mining. In some recent studies, ensemble classification has
been widely used in concept drift detection, however, most of them regard
classification accuracy as a criterion for judging whether concept drift
happening or not. Information entropy is an important and effective method for
measuring uncertainty. Based on the information entropy theory, a new algorithm
using information entropy to evaluate a classification result is developed. It
uses ensemble classification techniques, and the weight of each classifier is
decided through the entropy of the result produced by an ensemble classifiers
system. When the concept in data streams changing, the classifiers' weight
below a threshold value will be abandoned to adapt to a new concept in one
time. In the experimental analysis section, six databases and four proposed
algorithms are executed. The results show that the proposed method can not only
handle concept drift effectively, but also have a better classification
accuracy and time performance than the contrastive algorithms.