Concept-cognitive computing system for dynamic classification

In the context of big data, organizations and individuals can often benefit from the data mining techniques, such as classification. However, decision-makers must quickly react to insights over time under dynamic environments. In this paper, we present a novel perspective, named concept-cognitive computing system (C3S or ConceptCS), to achieve dynamic classification learning over the partially labeled data and labeled data. More specifically, to store and consolidate knowledge, a concept falling space is first employed as a basic knowledge memory mechanism in C3S. Then, we design a new concept-cognitive process by means of simulating human learning processes, which can incorporate new information into the old knowledge. Finally, a strategy of constructing two different concept spaces is considered in our system when faced with the scenario of a partially labeled dynamic data. Although there exist significant differences between C3S and the conventional incremental learning methods in the learning paradigm, our proposed C3S still performs strong performance for dynamic classification in comparison with several state-of-the-art incremental learning approaches. In addition, the experiments on various datasets have demonstrated that our system can obtain a good performance on the partially labeled data and labeled data simultaneously in dynamic environments.