04. An ensemble ordinal outcome classifier for high-dimensional data

Several classification techniques for ordinal outcomes in high-dimensional data have been developed throughout the years. However, the performances of these techniques depend heavily on the evaluation criteria used, and it is usually not known a priori which technique will perform the best in any classification application. In this project, we propose an ensemble classifier, constructed by combining bagging and rank aggregation techniques that can provide an optimal classification of the ordinal outcomes in high-dimensional data. Our classifier internally uses several existing ordinal classification algorithms and combines them in a flexible way to adaptively produce results. Our approach optimizes the classification outcomes across multiple performance measures, such as Hamming score, Mean Absolute Error, Kendall's τ_b, and weighted Kappa, among others. Through various simulation studies, we will compare the performance of our proposed ensemble classifier with the individual algorithms, included in the ensemble, and illustrate that our more intricate approach achieves enhanced predictive performance. We will also show the utility of our ensemble classifier with applications on real high-dimensional genomics data. We will highlight the fact that when dealing with the complexity of ordinal outcomes in high-dimensional datasets, it might be reasonable to consider an ensemble classification algorithm combining several classifiers rather than relying on a single classifier.

Mid-Level

Knowledge

Women in Statistics and Data Science 2024