Department of Electrical and Information Engineering, University of Nairobi, Nairobi 30197, Kenya.
Biomed Res Int. 2019 Dec 14;2019:4085725. doi: 10.1155/2019/4085725. eCollection 2019.
Determining an optimal decision model is an important but difficult combinatorial task in imbalanced microarray-based cancer classification. Though the multiclass support vector machine (MCSVM) has already made an important contribution in this field, its performance solely depends on three aspects: the penalty factor C, the type of kernel, and its parameters. To improve the performance of this classifier in microarray-based cancer analysis, this paper proposes PSO-PCA-LGP-MCSVM model that is based on particle swarm optimization (PSO), principal component analysis (PCA), and multiclass support vector machine (MCSVM). The MCSVM is based on a hybrid kernel, i.e., linear-Gaussian-polynomial (LGP) that combines the advantages of three standard kernels (linear, Gaussian, and polynomial) in a novel manner, where the linear kernel is linearly combined with the Gaussian kernel embedding the polynomial kernel. Further, this paper proves and makes sure that the LGP kernel confirms the features of a valid kernel. In order to reveal the effectiveness of our model, several experiments were conducted and the obtained results compared between our model and other three single kernel-based models, namely, PSO-PCA-L-MCSVM (utilizing a linear kernel), PSO-PCA-G-MCSVM (utilizing a Gaussian kernel), and PSO-PCA-P-MCSVM (utilizing a polynomial kernel). In comparison, two dual and two multiclass imbalanced standard microarray datasets were used. Experimental results in terms of three extended assessment metrics (-score, -mean, and Accuracy) reveal the superior global feature extraction, prediction, and learning abilities of this model against three single kernel-based models.
确定最优决策模型是不平衡微阵列癌症分类中的一项重要而困难的组合任务。尽管多类支持向量机（MCSVM）在该领域已经做出了重要贡献，但它的性能仅取决于三个方面：惩罚因子 C、核的类型及其参数。为了提高该分类器在基于微阵列的癌症分析中的性能，本文提出了基于粒子群优化（PSO）、主成分分析（PCA）和多类支持向量机（MCSVM）的 PSO-PCA-LGP-MCSVM 模型。MCSVM 基于混合核，即线性-高斯-多项式（LGP），以新颖的方式结合了三种标准核（线性、高斯和多项式）的优点，其中线性核与嵌入多项式核的高斯核线性组合。此外，本文证明并确保 LGP 核具有有效核的特征。为了揭示我们模型的有效性，进行了几次实验，并在我们的模型和其他三个基于单内核的模型（分别是利用线性核的 PSO-PCA-L-MCSVM、利用高斯核的 PSO-PCA-G-MCSVM 和利用多项式核的 PSO-PCA-P-MCSVM）之间进行了比较。在比较中，使用了两个双类和两个多类不平衡标准微阵列数据集。根据三个扩展评估指标（-score、-mean 和 Accuracy）的实验结果，该模型表现出优越的全局特征提取、预测和学习能力，优于三个基于单内核的模型。
