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Multiobjective feature selection for microarray data via distributed parallel algorithms

机译：通过分布式并行算法选择微阵列数据的多目标特征

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Many real-world problems are large in scale and hence difficult to address. Due to the large number of features in microarray datasets, feature selection and classification are even more challenging for such datasets. Not all of these numerous features contribute to the classification task, and some even impede performance. Through feature selection, a feature subset that contains only a small quantity of essential features can be generated to increase the classification accuracy and significantly reduce the time consumption. In this paper, we construct a multiobjective feature selection model that simultaneously considers the classification error, the feature number and the feature redundancy. For this model, we propose several distributed parallel algorithms based on different encodings and an adaptive strategy. Additionally, to reduce the time consumption, various tactics are employed, including a feature number constraint, distributed parallelism and sample-wise parallelism. For a batch of microarray datasets, the proposed algorithms are superior to several state-of-the-art multiobjective evolutionary algorithms in terms of both effectiveness and efficiency. (C) 2019 Published by Elsevier B.V.
机译：许多现实世界中的问题规模庞大，因此难以解决。由于微阵列数据集中有大量特征，因此对于此类数据集，特征选择和分类更具挑战性。并非所有这些众多功能都有助于分类任务，甚至有些功能会妨碍性能。通过特征选择，可以生成仅包含少量基本特征的特征子集，以提高分类准确性并显着减少时间消耗。在本文中，我们构造了一个多目标特征选择模型，该模型同时考虑了分类误差，特征编号和特征冗余。对于该模型，我们提出了几种基于不同编码和自适应策略的分布式并行算法。另外，为了减少时间消耗，采用了各种策略，包括特征数约束，分布式并行和按样本并行。对于一批微阵列数据集，在有效性和效率上，所提出的算法均优于几种最新的多目标进化算法。 （C）2019由Elsevier B.V.发布

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