Incomplete Data Transfer Calibration Classification

Classification of incomplete data remains a challenging task since the distribution of training and test sets may be inconsistently caused by missing values. To address such a problem, this paper investigates an incomplete data transfer calibration classification (IDTC) method based on Dempster-Shafer theory to improve the accuracy by modeling uncertainty and imprecision due to missing values. The proposed IDTC method consists of three aspects. First, incomplete samples are imputed by complete neighbors, and the trained basic classifier then classifies the test sample to obtain the preliminary result. Second, a data transfer framework is presented to map the test sample to the training set. Afterward, the mapped training samples optimize multiple calibration matrices to calibrate the preliminary classification result. The calibration matrix is learned by minimizing the deviation between the classification results of mapped training samples and the truth. Third, each calibrated classification result is considered as a piece of evidence under Dempster-Shafer theory. As a result, these pieces of evidence with different discounting factors are fused to make the final decision. Finally, the effectiveness of the proposed IDTC is widely validated on real datasets by critically comparing to other typical methods.