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Gene selection using hybrid binary black hole algorithm and modified binary particle swarm optimization

机译：基因选择使用混合二进制黑洞算法和修改二元粒子群优化

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摘要

In cancer classification, gene selection is an important data preprocessing technique, but it is a difficult task due to the large search space. Accordingly, the objective of this study is to develop a hybrid meta-heuristic Binary Black Hole Algorithm (BBHA) and Binary Particle Swarm Optimization (BPSO) (4-2) model that emphasizes gene selection. In this model, the BBHA is embedded in the BPSO (4-2) algorithm to make the BPSO (4-2) more effective and to facilitate the exploration and exploitation of the BPSO (4-2) algorithm to further improve the performance. This model has been associated with Random Forest Recursive Feature Elimination (RF-RFE) prefiltering technique. The classifiers which are evaluated in the proposed framework are Sparse Partial Least Squares Discriminant Analysis (SPLSDA); k-nearest neighbor and Naive Bayes. The performance of the proposed method was evaluated on two benchmark and three clinical microarrays. The experimental results and statistical analysis confirm the better performance of the BPSO (4-2)-BBHA compared with the BBHA, the BPSO (4-2) and several state-of-the-art methods in terms of avoiding local minima, convergence rate, accuracy and number of selected genes. The results also show that the BPSO (4-2)-BBHA model can successfully identify known biologically and statistically significant genes from the clinical datasets.
机译：在癌症分类中，基因选择是一个重要的数据预处理技术，但由于大搜索空间，这是一项艰巨的任务。因此，本研究的目的是开发一种强调基因选择的混合元启发式二进制黑洞算法（BBHA）和二元粒子群优化（BPSO）（4-2）模型。在该模型中，BBHA嵌入了BPSO（4-2）算法中，使BPSO（4-2）更有效，并促进BPSO（4-2）算法的勘探和开发，以进一步提高性能。该模型与随机森林递归特征消除（RF-RFE）预翻腾技术有关。在所提出的框架中评估的分类器是稀疏的部分最小二乘判别分析（SPLSDA）; k最近邻居和天真的贝父。在两个基准和三种临床微阵列中评估了所提出的方法的性能。实验结果和统计分析证实了与BBHA，BPSO（4-2）和几种最先进的方法相比，BPSO（4-2）-BBA的性能更好，避免了局部最小值，收敛选择基因的速率，准确性和数量。结果还表明，BPSO（4-2）-BBHA模型可以成功地从临床数据集中鉴定已知的生物学和统计学显着的基因。

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