Knowledge-based system for three-way decision-making under uncertainty

Knowledge-based systems developed based on Dempster-Shafer theory and prospect theory enhances decision-making under uncertainty. But at times, the traditional two-way decision approach may not be able to suggest a suitable decision confidently. This work proposes a three-way decision support system which divides the alternatives into three disjoint sets. Nonparametric Gaussian kernel and mid-range values are used to compute basic probabilities and reference points, respectively. The difference between basic probabilities and reference points is considered for assigning gain-loss values based on the value function from prospect theory. Ten publicly available benchmark data sets are considered, and the effectiveness of the proposed system is affirmed by comparing its performance with traditional machine learning models and other relevant decision-making systems in the literature. A case study related to evaluation of candidates is included, and it is also compared with other reference point estimation methods. From the results, it can be inferred that considering mid-range values as reference generates a preference order that is intuitive and compliable.