A kernel PLS based classification method with missing data handling

We provide a data classification mechanism with missing data handling based on kernel partial least squares (kernel PLS) and discriminant analysis (kernel PLSDA). The novelty of the method is that class variables are used for validation of the missing values imputation. Likewise, this paper is first in utilizing the kernel PLS in handling and classifying missing data. By experimentally comparing the results of different classification methods including missing data handling on three opened biomedical datasets (Arrhythmia, Mammographic Mass, and Pima Indians Diabetes at UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/datasets.html), we found that the proposed kernel PLS plus kernel PLSDA yielded better accuracies than the existing methods.