Multi-Criteria Decision Analysis (MCDA) supports decision-making in complex scenarios involving multiple conflicting criteria. The Characteristic Objects METhod (COMET) is a relatively recent MCDA technique that relies on expert-based pairwise comparisons to evaluate alternatives. Despite its growing adoption in MCDA domain the COMET method have some limitations, such as scalability issues due to the rapid increase in the number of required pairwise comparisons as the number of criteria grows. To address this limitation, we propose two extensions to the Triad Support algorithm, which aim to significantly reduce the number of comparisons needed while preserving the model’s accuracy. The first enhancement involves reordering Characteristic Objects for easier comparison process, while the second use expert-defined criteria types to predefine characteristic values relations. Under the assumption of transitive and consistent expert preferences, these extensions produce the same decision model as the original COMET method. We also provide comparative experiments that demonstrates how both of proposed methods outperform the original COMET and Triad Support algorithm in terms pairwise comparison reduction. These improvements facilitate the application of COMET to larger and more complex decision problems.