用混合特征选择与粒子群优化提升基因芯片分类准确率
ATwo-Stage Ensemble Feature Selection and Particle Swarm Optimization Approach for Micro-Array Data Classification in Distributed Computing Environments
- 融合过滤器与包装器方法筛选关键基因特征，再用粒子群优化找最优组合
- 云环境64位虚拟机上对6个数据集分类准确率达94.6%至99.58%
- 适合生物信息学、医疗诊断领域研究者参考，尤其关注高维数据处理
微阵列技术产生的高维数据给机器学习算法带来挑战，尤其是在降维和样本不平衡方面。为此，本文提出一种混合集成特征选择方法，结合多数投票分类器进行微阵列分类。综合使用互信息（MI）、卡方检验、方差阈值（VT）、LASSO、方差分析（ANOVA）和递归特征消除（RFE）等过滤与包装方法，并通过粒子群优化（PSO）选择最优特征子集。该人工智能方法采用多数投票分类器，集成逻辑回归（LR）、随机森林（RF）和极端梯度提升（XGBoost）等多种模型，以增强整体性能与准确性。实验在本地与云环境验证模型有效性。云环境中使用3台8、16、64位虚拟中央处理器（vCPU），结果显示，64位vCPU下对6个微阵列数据集——混合谱系白血病（MLL）、白血病、小圆蓝细胞肿瘤（SRBCT）、淋巴瘤、卵巢癌和肺癌——的分类准确率分别达到95.89%、97.50%、99.13%、99.58%、99.11%和94.60%，验证了该方法在本地与云环境中的有效性。
原文摘要 · Abstract (English)
High dimensionality in datasets produced by microarray technology presents a challenge for Machine Learning (ML) algorithms, particularly in terms of dimensionality reduction and handling imbalanced sample sizes. To mitigate the explained problems, we have proposedhybrid ensemble feature selection techniques with majority voting classifier for micro array classi f ication. Here we have considered both filter and wrapper-based feature selection techniques including Mutual Information (MI), Chi-Square, Variance Threshold (VT), Least Absolute Shrinkage and Selection Operator (LASSO), Analysis of Variance (ANOVA), and Recursive Feature Elimination (RFE), followed by Particle Swarm Optimization (PSO) for selecting the optimal features. This Artificial Intelligence (AI) approach leverages a Majority Voting Classifier that combines multiple machine learning models, such as Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), to enhance overall performance and accuracy. By leveraging the strengths of each model, the ensemble approach aims to provide more reliable and effective diagnostic predictions. The efficacy of the proposed model has been tested in both local and cloud environments. In the cloud environment, three virtual machines virtual Central Processing Unit (vCPU) with size 8,16 and 64 bits, have been used to demonstrate the model performance. From the experiment it has been observed that, virtual Central Processing Unit (vCPU)-64 bits provides better classification accuracies of 95.89%, 97.50%, 99.13%, 99.58%, 99.11%, and 94.60% with six microarray datasets, Mixed Lineage Leukemia (MLL), Leukemia, Small Round Blue Cell Tumors (SRBCT), Lymphoma, Ovarian, andLung,respectively, validating the effectiveness of the proposed modelin bothlocalandcloud environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。