Maximum A Posteriori EM MCE Logistic LASSO for learning fuzzy measures

Particle swarm optimization for determining fuzzy measures from data

“…Fuzzy measures [31,33,36,68] and fuzzy integrals [15,59,64,66,70] have been applied successfully in multi-attributes decision-making [18,53,54], classification [52,65,69], information fusion [3,5,11,43,49], nonlinear multi-regression [30], feature selection [19,44] and image processing [24,32,34,35]. The reason of success is from the highly non-additive and non-linear characteristics of fuzzy measures and fuzzy integrals.…”

“…So in this paper, we do not discuss this type. Recently, Mendez-Vazquez [34] presented a new method based on ''Least Absolute Shrink-age and Selection Operator (LASSO)'' and ''Expectation-Maximization (EA)''. This method can keep the monotonicity constraints easily, and does not need huge storage.…”