Active Learning Classification of Drifted Streaming Data

AbstractObjects being recognized may arrive continuously to a classifier in the form of data stream, therefore contemporary classification systems have to make a decision not only on the basis of the static data, but on the data in motion as well. Additionally, we would like to start a classifier exploitation as soon as possible, then the models which can improve their models during exportation are very desirable. Basically, we may produce the model on the basis a few learning objects only and then we use and improve the classifier when new data comes. This concept is still vibrant and may be used in the plethora of practical cases. Nevertheless, constructing such a system we should realize, that we have the limited resources (as memory and computational power) at our disposal. Additionally, during the exploitation of a classifier system the chosen characteristic of the classifier model may change within a time. This phenomena is called concept drift and may lead the deep deterioration of the classification performance. This work deals with the data stream classification with the presence of concept drift. We propose a novel classifier training algorithm based on the sliding windows approach, which allows us to implement forgetting mechanism, i.e., that old objects come from outdated model will not be taken into consideration during the classifier updating and on the other hand we assume that only part of arriving examples can be labeled, because we assume that we have a limited budget for labeling. We will employ active learning paradigm to choose an “interesting” objects to be be labeled. The proposed approach has been evaluated on the basis of the computer experiments carried out on the data streams. Obtained results confirmed the usability of proposed method to the smoothly drifted data stream classification