Parallel reduced multi-class contour preserving classification

摘要

Multi-class contour preserving classification is a contour conservancy technique that synthesizes two types of vectors; fundamental multi-class outpost vectors (FMCOVs) and additional multi-class outpost vectors (AMCOVs), at the judging border between classes of data to improve the classification accuracy of the feed-forward neural network. However, the number of both new vectors is tremendous, resulting in a significantly prolonged training time. Reduced multi-class contour preserving classification provides three practical methods to lessen the number of FMCOVs and AMCOVs. Nevertheless, the three reduced multi-class outpost vector methods are serial and therefore have limited applicability on modern machines with multiple CPU cores or processors. This paper presents the methodologies and the frameworks of the three parallel reduced multi-class outpost vector methods that can effectively utilize thread-level parallelism and process-level parallelism to (1) substantially lessen the number of FMCOVs and AMCOVs, (2) efficiently increase the speedups in execution times to be proportional to the number of available CPU cores or processors, and (3) significantly increase the classification performance (accuracy, precision, recall, and F1 score) of the feed-forward neural network. The experiments carried out on the balanced and imbalanced real-world multi-class data sets downloaded from the UCI machine learning repository confirmed the reduction performance, the speedups, and the classification performance aforementioned.