TABLE OF CONTENTS Most classification methods are based on the assumption that the historic data involved in building and verifying the model is the best estimator of what will happen in the future. One important factor that must not be set aside is the time factor. As more data is accumulated into the problem domain, incrementally over time, one must examine whether the new data agrees with the previous datasets and make the relevant assumptions about the future. This work presents a new change detection methodology, with a set of statistical estimators. These changes can be detected independently of the data mining algorithm, which is used for constructing the corresponding model. By implementing the novel approach on a set of artificially generated datasets, all significant changes were detected in the relevant periods. Also, in the real-world datasets evaluation, the method produced similar results.
Keywords: Classification; incremental learning; time series; change detection; info-fuzzy network.
As mass of data is incrementally accumulated into large databases over time, we tend to believe that the new data "acts" somehow resembling to the prior knowledge we have on the operation or facts that it describes. Change detection in time series is not a new subject and it has always...
