Learning in a Fuzzy Random Forest ensemble from imperfect data

Instrument errors or noise interference during experiments may lead to incomplete data when measuring a specific attribute. Obtaining models from imperfect data is a topic currently being treated with more interest. In this paper, we present the learning phase of a Fuzzy Random Forest ensemble for classification from imperfect data. We perform experiments with imperfect datasets created for this purpose and datasets used in other papers to show the express the true nature of imperfect information.