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A Hybrid Gene Selection Method Based on ReliefF and Ant Colony Optimization Algorithm for Tumor Classification

机译：基于ReliefF和蚁群优化算法的混合基因选择方法在肿瘤分类中的应用

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摘要

For the DNA microarray datasets, tumor classification based on gene expression profiles has drawn great attention, and gene selection plays a significant role in improving the classification performance of microarray data. In this study, an effective hybrid gene selection method based on ReliefF and Ant colony optimization (ACO) algorithm for tumor classification is proposed. First, for the ReliefF algorithm, the average distance among k nearest or k non-nearest neighbor samples are introduced to estimate the difference among samples, based on which the distances between the samples in the same class or the different classes are defined, and then it can more effectively evaluate the weight values of genes for samples. To obtain the stable results in emergencies, a distance coefficient is developed to construct a new formula of updating weight coefficient of genes to further reduce the instability during calculations. When decreasing the distance between the same samples and increasing the distance between the different samples, the weight division is more obvious. Thus, the ReliefF algorithm can be improved to reduce the initial dimensionality of gene expression datasets and obtain a candidate gene subset. Second, a new pruning rule is designed to reduce dimensionality and obtain a new candidate subset with the smaller number of genes. The probability formula of the next point in the path selected by the ants is presented to highlight the closeness of the correlation relationship between the reaction variables. To increase the pheromone concentration of important genes, a new phenotype updating formula of the ACO algorithm is adopted to prevent the pheromone left by the ants that are overwhelmed with time, and then the weight coefficients of the genes are applied here to eliminate the interference of difference data as much as possible. It follows that the improved ACO algorithm has the ability of the strong positive feedback, which quickly converges to an optimal solution through the accumulation and the updating of pheromone. Finally, by combining the improved ReliefF algorithm and the improved ACO method, a hybrid filter-wrapper-based gene selection algorithm called as RFACO-GS is proposed. The experimental results under several public gene expression datasets demonstrate that the proposed method is very effective, which can significantly reduce the dimensionality of gene expression datasets, and select the most relevant genes with high classification accuracy.
机译：对于DNA微阵列数据集，基于基因表达谱的肿瘤分类引起了极大的关注，并且基因选择在改善微阵列数据的分类性能中起着重要作用。提出了一种基于ReliefF和蚁群优化（ACO）算法进行肿瘤分类的有效杂种基因选择方法。首先，对于ReliefF算法，引入k个最近的样本或k个非最近的邻居样本之间的平均距离以估计样本之间的差异，基于此，定义相同类别或不同类别的样本之间的距离，然后它可以更有效地评估样品基因的权重值。为了在紧急情况下获得稳定的结果，开发了一种距离系数，以构建更新基因权重系数的新公式，以进一步减少计算过程中的不稳定性。当减少相同样本之间的距离而增加不同样本之间的距离时，权重划分更加明显。因此，可以改进ReliefF算法以减少基因表达数据集的初始维数并获得候选基因子集。其次，设计了一种新的修剪规则以减少维数并获得具有较少基因数量的新候选子集。提出了由蚂蚁选择的路径中下一点的概率公式，以突出反应变量之间相关关系的紧密性。为了提高重要基因的信息素浓度，采用了一种新的ACO算法表型更新公式，以防止蚂蚁随时间流逝而留下的信息素，然后在此处应用基因的权重系数消除干扰。差异数据。由此可见，改进的ACO算法具有较强的正反馈能力，可以通过信息素的积累和更新迅速收敛到最优解。最后，通过结合改进的ReliefF算法和改进的ACO方法，提出了一种基于混合滤波器包装的基因选择算法RFACO-GS。在多个公共基因表达数据集下的实验结果表明，该方法非常有效，可以显着降低基因表达数据集的维数，并选择分类准确度最高的相关基因。

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