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Multiple decision attributes were interrelated in highly complex ways.
Patients consider multiple decision attributes related to personalized medical risk as well as life circumstance, and these attributes are interrelated in complex ways.
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Practical issues such as multiple conflicting objectives [35, 90], multiple decisions [95, 104], attribute selection [79] are catching more attentions.
ANP is a multiple criteria decision making (MCDM) method that can accommodate interdependency among decision attributes.
These results shed light on decision attributes that influence patient CHC treatment decisions and reveal complex relationships between medical attributes and life circumstance attributes in these decisions.
In the reduced space, the attribute vectors of the peptides are then used to construct a Random Forest (RF) classifier [ 239], an ensemble learner based on combining the output of multiple decision trees.
In some real-world multiple attribute decision making (MADM) problems, a decision maker can strategically set attribute weights to obtain her/his desired ranking of alternatives, which we call the strategic weight manipulation of the MADM.
In real life, underground mining method selection is one of the multiple attribute decision making (MADM) problems and decision makers have always some difficulties in making the right decision in the multiple criteria environment.
Wei [41] applied IIFWGA aggregation functions to deal with dynamic multiple attribute decision making where all the attribute values are expressed in intuitionistic fuzzy numbers or interval-valued intuitionistic fuzzy numbers.
Decisions on attribute weights are important problems in multiple attribute decision making.
Methods of MCDA can be subdivided into Multiple Attribute Decision Making (MADM) and Multiple Objective Decision Making (MODM) (Malczewski, 1999a).
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