Multifactor dimensionality reduction

Multifactor dimensionality reduction (MDR) is a statistical approach, also used in machine learning automatic approaches,^[1] for detecting and characterizing combinations of attributes or independent variables that interact to influence a dependent or class variable.^{[2][3][4][5][6][7][8]} MDR was designed specifically to identify nonadditive interactions among discrete variables that influence a binary outcome and is considered a nonparametric and model-free alternative to traditional statistical methods such as logistic regression.

The basis of the MDR method is a constructive induction or feature engineering algorithm that converts two or more variables or attributes to a single attribute.^[9] This process of constructing a new attribute changes the representation space of the data.^[10] The end goal is to create or discover a representation that facilitates the detection of nonlinear or nonadditive interactions among the attributes such that prediction of the class variable is improved over that of the original representation of the data.