Monotone models for prediction in data mining

This dissertation studies the incorporation of monotonicity constraints as a type of domain knowledge into a data mining process. Monotonicity constraints are enforced at two stages¿data preparation and data modeling. The main contributions of the research are a novel procedure to test the degree of monotonicity of a real data set, a greedy algorithm to transform non-monotone into monotone data, and extended and novel approaches for building monotone decision models. The results from simulation and real case studies show that enforcing monotonicity can considerably improve knowledge discovery and facilitate the decision-making process for end-users by deriving more accurate, stable and plausible decision models.