Dynamic classification decision making is a crucial issue in management decision making and data min-ing, which is widely applied in different areas such as human-machine collaborative decision making, network intrusi...
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Dynamic classification decision making is a crucial issue in management decision making and data min-ing, which is widely applied in different areas such as human-machine collaborative decision making, network intrusion detection, and traffic data stream mining. However, the existing strategies of static classification decision making are always unable to achieve desired outcomes in ill-structured domains, as the standard machine learning approaches mainly focus on static learning, which is not suitable to mine evolving dynamic data to support decision making. In addition, the main factors regarding incor-rect classification predictions are also important for knowledge management and decision making, which is often ignored in many standard learning systems. Therefore, inspired by the idea of divide and con-quer, we in this article propose a novel dynamic concept learning framework, namely granular concept -cognitive computing system (gC3S), for dynamic classification decision making by transforming instances into concepts. More specifically, to better characterize the process of dynamic classification decision mak-ing, we give the objective function of gC3S via mathematical programming theory. For management deci-sion making, gC3S emphasizes tracing the corresponding approximate concepts via the incorrect classifi-cation predictions. Finally, we also apply gC3S to traffic data stream mining, and the experimental results on the different real-world situations further demonstrate that our approach is very effective for dynamic classification decision making.(c) 2022 Elsevier B.V. All rights reserved.
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