Accountable Capability Improvement Based on Interpretable Capability Evaluation Using Belief Rule Base

A new approach is proposed in this study for accountable capability improvement based on interpretable capability evaluation using the belief rule base (BRB). Firstly, a capability evaluation model is constructed and optimized. Then, the key sub-capabilities are identified by quantitatively calculating the contributions made by each sub-capability to the overall capability. Finally, the overall capability is improved by optimizing the identified key sub-capabilities. The theoretical contributions of the proposed approach are as follows. (i) An interpretable capability evaluation model is constructed by employing BRB which can provide complete access to decision-makers. (ii) Key sub-capabilities are identified according to the quantitative contribution analysis results. (iii) Accountable capability improvement is carried out by only optimizing the identified key sub-capabilities. Case study results show that “Surveillance”, “Positioning”, and “Identification” are identified as key sub-capabilities with a summed contribution of 75.55% in an analytical and deducible fashion based on the interpretable capability evaluation model. As a result, the overall capability is improved by optimizing only the identified key sub-capabilities. The overall capability can be greatly improved from 59.20% to 81.80% with a minimum cost of 397. Furthermore, this paper also investigates how optimizing the BRB with more collected data would affect the evaluation results: only optimizing “Surveillance” and “Positioning” can also improve the overall capability to 81.34% with a cost of 370, which thus validates the efficiency of the proposed approach.