DNA微阵列分类中最优集成分类器的指定遗传算法

Sung-Bae Cho, Chanho Park
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引用次数: 7

摘要

随着微阵列技术的发展,近十年来,微阵列数据的分类已成为一个重要的课题。从各种特征选择方法和分类器中,由于算法的不完备、数据本身的缺陷等原因,很难找到一种完美的方法对微阵列数据进行分类。本文提出了一个复杂的集成这些特征和分类器,以获得较高的分类性能。利用特定的遗传算法在合理的时间内得到不同的特征和分类器集合。在两个已知数据集上的实验结果表明,该方法找到了许多优于其他单个分类器的良好集成。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Speciated GA for optimal ensemble classifiers in DNA microarray classification
With the development of microarray technology, the classification of microarray data has risen as an important topic over the past decade. From various feature selection methods and classifiers, it is very hard to find a perfect method to classify microarray data due to the incompleteness of algorithms, the defects of data, etc. This paper proposes a sophisticated ensemble of such features and classifiers to obtain high classification performance. Speciated genetic algorithm has been exploited to get the diverse ensembles of features and classifiers in a reasonable time. Experimental results with two well-known datasets indicate that the proposed method finds many good ensembles that are superior to other individual classifiers.
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