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A hybrid gene selection approach for microarray data classification using cellular learning automata and ant colony optimization

机译:基于细胞学习自动机和蚁群优化的杂交基因选择方法用于微阵列数据分类

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摘要

This paper proposes an approach for gene selection in microarray data. The proposed approach consists of a primary filter approach using Fisher criterion which reduces the initial genes and hence the search space and time complexity. Then, a wrapper approach which is based on cellular learning automata (CLA) optimized with ant colony method (ACO) is used to find the set of features which improve the classification accuracy. CLA is applied due to its capability to learn and model complicated relationships. The selected features from the last phase are evaluated using ROC curve and the most effective while smallest feature subset is determined. The classifiers which are evaluated in the proposed framework are K-nearest neighbor; support vector machine and naive Bayes. The proposed approach is evaluated on 4 microarray datasets. The evaluations confirm that the proposed approach can find the smallest subset of genes while approaching the maximum accuracy. (C) 2016 Elsevier Inc. All rights reserved.
机译:本文提出了一种在微阵列数据中进行基因选择的方法。所提出的方法包括使用费舍尔准则的主要过滤器方法,该方法减少了初始基因,从而减少了搜索空间和时间复杂度。然后,使用基于蚁群方法(ACO)优化的细胞学习自动机(CLA)的包装方法来查找可提高分类准确性的特征集。之所以使用CLA,是因为它具有学习和建模复杂关系的能力。使用ROC曲线评估从最后一个阶段选择的特征,并确定最有效的特征子集。在提出的框架中评估的分类器是K近邻。支持向量机和朴素贝叶斯。在4个微阵列数据集上评估了提出的方法。评估证实,所提出的方法可以找到最小的基因子集,同时又可以达到最大的准确性。 (C)2016 Elsevier Inc.保留所有权利。

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