A semi-supervised concept-cognitive computing system for dynamic classification decision making with limited feedback information

In dynamic environments, making classification decisions based on classical intelligent decision support systems is a challenge, as the classification performance of decision-making and the time-cost of learning need to be considered simultaneously. Moreover, many tasks of classification decisions lack label information because annotating data is time-consuming, labor-intensive and expensive process. This means that some standard intelligent decision support systems will perform inferior performance if they cannot dynamically make full use of the information behind abundant unlabeled data. Therefore, by incorporating knowledge representation and dynamic updating mechanisms into concept learning processes, we introduce a novel dynamic concept learning approach, namely semi-supervised concept-cognitive computing system (s2C3S), for making classification decisions by jointly utilizing some labeled data and abundant unlabeled data under dynamic environments. A theoretical analysis has shown that the proposed s2C3S can achieve significantly lower computational costs and higher classification accuracies than the existing incremental K Nearest Neighbor method (IKNN) and concept-cognitive computing system (C3S). The experimental results on various datasets further demonstrated that our system is effective for dynamic classification decision-making with limited labeled data under dynamic learning processes. Additionally, s2C3S can also be applied to computer-assisted intelligent diagnosis from the given medical images (such as chest X-ray images) dynamically and accurately.