Enhancements for Sliding Window based Stream Classification

In stream mining, there are several limitations on the classification process, since the time and resource are limited. The data is read only once and the whole history of data can not be stored. There are several methods developed so far such as stream based adaptations of decision trees, nearest-neighbor methods and neural network classifiers. This paper presents new enhancements on sliding window based classification methods. As the first modification, we use the traditional kNN (K-Nearest Neighbors) method in a sliding window and include the mean of the previous instances as a nearest neighbor instance. By this, we aim to associate the behaviour pattern coming from the past and current state of data. We call this method as m-kNN (Mean extended kNN). As the second enhancement, we generate an ensemble classifier as the combination of our m-kNN with traditional kNN and Naive Bayes classifier. We call this method CSWB (Combined Sliding Window Based) classifier. We present the accuracy of our methods on several datasets in comparison to the results against the state-of-the-art classifiers MC-NN (Micro Cluster Nearest Neighbor) and VHT (Vertical Hoeffding Tree). The results reveal that the proposed method performs better for several data sets and have potential for further improvement.