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Cell Group Recognition Method Based on Adaptive Mutation PSO-SVM

机译：基于自适应变异PSO-SVM的细胞群识别方法

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摘要

The increased volume and complexity of flow cytometry (FCM) data resulting from the increased throughput greatly boosts the demand for reliable statistical methods for the analysis of multidimensional data. The Support Vector Machines (SVM) model can be used for classification recognition. However, the selection of penalty factor c and kernel parameter g in the model has a great influence on the correctness of clustering. To solve the problem of parameter optimization of the SVM model, a support vector machine algorithm of particle swarm optimization (PSO-SVM) based on adaptive mutation is proposed. Firstly, a large number of FCM data were used to carry out the experiment, and the kernel function adapted to the sample data was selected. Then the PSO algorithm of adaptive mutation was used to optimize the parameters of the SVM classifier. Finally, the cell clustering results were obtained. The method greatly improves the clustering correctness of traditional SVM. That also overcomes the shortcomings of PSO algorithm, which is easy to fall into local optimum in the iterative optimization process and has poor convergence effect in dealing with a large number of data. Compared with the traditional SVM algorithm, the experimental results show that, the correctness of the method is improved by 19.38%. Compared with the cross-validation algorithm and the PSO algorithm, the adaptive mutation PSO algorithm can also improve the correctness of FCM data clustering. The correctness of the algorithm can reach 99.79% and the time complexity is relatively lower. At the same time, the method does not need manual intervention, which promotes the research of cell group identification in biomedical detection technology.
机译：由于吞吐量的增加，流式细胞术（FCM）数据的数量增加和复杂性大大提高了对用于多维数据分析的可靠统计方法的需求。支持向量机（SVM）模型可用于分类识别。但是，模型中惩罚因子c和核参数g的选择对聚类的正确性有很大影响。为解决支持向量机模型参数优化的问题，提出了一种基于自适应变异的支持向量机粒子群算法（PSO-SVM）。首先，使用大量的FCM数据进行实验，然后选择适合样本数据的核函数。然后采用自适应变异的PSO算法对SVM分类器的参数进行优化。最后，获得了细胞聚类结果。该方法大大提高了传统支持向量机的聚类正确性。这也克服了PSO算法的缺点，该算法容易在迭代优化过程中陷入局部最优，并且在处理大量数据时收敛效果较差。实验结果表明，与传统的支持向量机算法相比，该方法的正确性提高了19.38％。与交叉验证算法和PSO算法相比，自适应变异PSO算法还可以提高FCM数据聚类的正确性。该算法的正确率可以达到99.79％，时间复杂度相对较低。同时，该方法不需要人工干预，从而促进了生物医学检测技术中细胞群识别的研究。

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