Unveiling the potential of graph coloring in feature set partitioning: A study on high-dimensional datasets

Abstract

The branch of machine learning known as multi-view ensemble learning (MEL) is young and evolving quickly. The learning procedure in this case makes use of subsets of different features from the same dataset, and the prediction produced is then combined. The vertical partition of the dataset in regard to the portion of the feature set in a single source dataset is referred to as the view. View construction is a crucial job in MEL because an adequate number of good-quality views improves MEL’s performance. A well-known method of dividing up the nodes of a graph is called “graph coloring”, which involves giving each vertex a unique color so that no two neighboring vertex pairs share the same color. This approach can be utilized in a number of diverse fields including clustering. In this study, high-dimension features are partitioned using graph coloring, which is used to perform heterogeneous feature grouping. In order to automatically create views in MEL over high-dimensional datasets, the Graph coloring-based feature set partitioning ($\mathrm{GC}$-FSP) technique is used. A support vector machine and artificial neural network have been used with 15 high-dimensional data sets to demonstrate the efficacy of the $\mathrm{GC}$-FSP based MEL framework. Compared to single-view learning and other cutting-edge FSP-based MEL techniques, the results show that it is successful in enhancing classification performance. The outcomes have undergone non-parametric statistical study and the intended MEL framework has produced improved classification accuracy that is both acceptable and accurate.