To make better use of the cloud computing technology, and to overcome the computing and storage requirements which increase rapidly with the number of training samples, in this paper, a new parallel algorithm is proposed—Parallel Regularized Multiple-Criteria Linear Programming (PRMCLP) algorithm—The RMCLP model is converted into a unconstrained optimization problem, and then, in the parallel version, it is split into several tasks, where each part is mapped and computed on a separate processor. This approach enables us to obtain efficiently the final optimization solution of the whole classification problem. At last, we apply this algorithm to Medical data classification. All experiments show that our method and approach greatly increases the training speed of RMCLP in the parallel case.

中文翻译：

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并行RMCLP分类算法及其在医学数据上的应用

为了更好地利用云计算技术，克服随着训练样本数量快速增长的计算和存储需求，本文提出了一种新的并行算法——并行正则化多准则线性规划（PRMCLP）算法。 ——将 RMCLP 模型转化为无约束优化问题，然后在并行版本中，将其拆分为多个任务，其中每个部分都在单独的处理器上进行映射和计算。这种方法使我们能够有效地获得整个分类问题的最终优化解决方案。最后，我们将该算法应用于医学数据分类。所有实验表明，我们的方法和方法在并行情况下大大提高了 RMCLP 的训练速度。