Abstract
This paper addresses the classification problem with imperfect Data Streams. More precisely, it extends standard CVFDT to handle uncertainty in both building and classification procedures. Uncertainty here is represented by possibility distributions. The first part investigates the issue of building decision trees from Data Streams with uncertain attribute values by developing a non-specificity based information gain as the attribute selection measure which, in our case, is more appropriate than the standard selection measure based on Shannon entropy. The extended approach so-called Possibilistic Very Fast Decision Tree for Uncertain Data Streams (Poss-CVFDT) offers a more flexible building procedure. The second part addresses the classification phase. More specifically, it investigates the issue of predicting the class value of new instances presented with certain and/or uncertain attribute values.
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Hamroun, M., Gouider, M.S. (2015). Possibilistic Very Fast Decision Tree for Uncertain Data Streams. In: Neves-Silva, R., Jain, L., Howlett, R. (eds) Intelligent Decision Technologies. IDT 2017. Smart Innovation, Systems and Technologies, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-319-19857-6_18
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DOI: https://doi.org/10.1007/978-3-319-19857-6_18
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