Hybrid-driven BRBCS-BOM with expert intervention and its application for abnormity recognition in electrolytic cell

Abstract

Belief rule-based classification system (BRBCS) is a useful model to handle classification problems. In our previous work, a novel data-driven BRBCS with batch-by-batch observation, online learning and multi-weights (BRBCS-BOM) is proposed. It can powerfully obtain knowledge from data. However, in some applications of industrial engineering, it can be hard to obtain enough training samples. The knowledge contained in data is not enough to deal with the problem well. It is necessary to adopt expert-driven BRBCS as an important supplement (hybrid-driven). In this paper, a hybrid-driven BRBCS-BOM with expert intervention (HBRBCS-BOM/E2) is proposed. It adopts a modified inheriting-and-learning hybrid-driven mode. Generally, it inherits the belief rules generated from training samples and make a secondary optimization for these inherited belief rules based on learning from expert-driven BRBCS. Moreover, a novel mode of expert intervention is proposed, based on the reliability evaluation. It can diversely-and-precisely obtain some important online new training samples for enhancement of data-knowledge, making hybrid-driven model better. The related experiments on an industrial engineering classification problem, called abnormity recognition of synthetical balance of material and energy, have demonstrated that the proposed HBRBCS-BOM/E2 not only makes an effective improvement for data-driven BRBCS-BOM from above two ways, but also has a more advanced performance compared with other existing high-performance BRBCS.