Measure Identification for the Choquet Integral: A Python Module

Abstract Fuzzy integrals are common concepts which used to aggregate input values in practical applications. Aggregation of inputs using fuzzy opens up numerous possibilities for modeling interaction, redundancy, and synergy inputs. However, need a measure start this aggregation process. This situation pushes us into the identification process becomes difficult due monotony condition exponential increase on number parameters. There literature many ways determine measures. One them is learning from data. In paper, our aim introduce new tool learn measures empirical It Python module finds that minimizes difference between computed expected outputs Choquet integral. addition, we study some properties particular, consider k -additive belief functions as well arbitrary Using these variety examine effect noisy data