Abstract
The resources of time and memory space are limited in data stream classification process. Hence, one should read the data only once and it is not possible to store the history as a whole. Therefore, when dealing with data streams, classification approaches in traditional data mining fall short and several enhancements are needed. In the literature, there are stream classifications methods such as stream based versions of nearest neighbor, decision tree based or neural network based methods. In our previous work, we proposed m-kNN (Mean Extended k-Nearest Neighbors) and CSWB (Combined Sliding Window Based) classifiers and presented the accuracy performances in comparison to other data stream classification methods from the literature. In this work, we present two new versions of CSWB, CSWB-e and CSWB-e2, such that our m-kNN classifier is combined with K* (K-Star) and C4.5, and with K* (K-Star) and Naive Bayes, respectively. In the experiments, accuracy of m-kNN, CSWB-e and CSWB-e2 are analyzed with new data sets in order to observe the relationship between window size and the accuracy. Additionally, the classification performance results for m-kNN are further analyzed and reported in precision, recall and f-score metrics in addition to accuracy.
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Maden, E., Karagoz, P. (2020). A Hybrid Sliding Window Based Method for Stream Classification. In: Fred, A., Salgado, A., Aveiro, D., Dietz, J., Bernardino, J., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2019. Communications in Computer and Information Science, vol 1297. Springer, Cham. https://doi.org/10.1007/978-3-030-66196-0_5
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