Maximum Likelihood Evidential Reasoning-Based Hierarchical Inference with Incomplete Data

Data mining requires a pre-processing task where data are prepared, cleaned, integrated, transformed, reduced and discretized to ensure data quality. Incomplete data are commonly encountered during data cleaning, which can have major impact on the conclusions that will be drawn from the data. In order to effectively carry out inferential modelling or decision making from incomplete independent variables or explanatory variables and consider different types of uncertainties, this paper adopts a data-driven inferential modelling approach, Maximum Likelihood Evidential Reasoning (MAKER) framework, which takes advantage of incomplete datasets without any imputation that may be required by other conventional machine learning methods. The MAKER framework reflects the plausibility of different values of missing data and expresses data-driven support for different values of missing data.