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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >Parallel reduced multi-class contour preserving classification
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Parallel reduced multi-class contour preserving classification

机译：平行减少多级轮廓保留分类

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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.
机译：多级轮廓保存分类是一种轮廓保护技术，用于合成两种类型的载体;基本的多级前哨向量（FMCOV）和额外的多级前哨向量（AMCOV），在数据类别之间的判断边界，以提高前馈神经网络的分类准确性。但是，这两个新载体的数量都是巨大的，导致训练时间显着延长。减少的多级轮廓保存分类提供了三种实用方法，以减少FMCovs和AMCOV的数量。尽管如此，三种减少的多级峰值载体方法是串行的，因此在具有多个CPU核心或处理器的现代机器上具有有限的适用性。本文介绍了三个平行减少的多级源载体方法的方法和框架，可以有效地利用螺纹级并行度和过程级并行性至（1）显着减少FMCovs和AMCOV的数量，（2）有效地增加执行时间与可用CPU核心或处理器的数量成比例的加速度，以及（3）显着提高了前馈神经网络的分类性能（准确性，精度，召回和F1分数）。从UCI机器学习存储库下载的平衡和不平衡现实世界多级数据集进行的实验证实了上述减少性能，加速和分类性能。

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