Microarray technology presents a challenge due to the large dimensionality of the data, which can be difficult to interpret. To address this challenge, the article proposes a feature extraction-based cancer classification technique coupled with artificial bee colony optimization (ABC) algorithm. The ABC-support vector machine (SVM) method is used to classify the lung cancer datasets and compared them with existing techniques in terms of precision, recall, F-measure, and accuracy. The proposed ABC-SVM has the advantage of dealing with complex nonlinear data, providing good flexibility. Simulation analysis was conducted with 30% of the data reserved for testing the proposed method. The results indicate that the proposed attribute classification technique, which uses fewer genes, performs better than other modalities. The classifiers, such as naïve Bayes, multi-class SVM, and linear discriminant analysis, were also compared and the proposed method outperformed these classifiers and state-of-the-art techniques. Overall, this study demonstrates the potential of using intelligent algorithms and feature extraction techniques to improve the accuracy of cancer diagnosis using microarray gene expression data.

中文翻译：

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基于SVM-ABC的癌症微阵列（基因表达）混合数据分类方法

由于数据维数很大，难以解释，微阵列技术提出了挑战。为了应对这一挑战，本文提出了一种基于特征提取的癌症分类技术与人工蜂群优化（ABC）算法相结合。使用ABC支持向量机（SVM）方法对肺癌数据集进行分类，并在精度、召回率、F-measure和准确度方面与现有技术进行比较。所提出的ABC-SVM的优点是可以处理复杂的非线性数据，提供良好的灵活性。为测试所提出的方法保留了30%的数据进行了仿真分析。结果表明，所提出的属性分类技术使用较少的基因，比其他模式表现更好。还对朴素贝叶斯、多类支持向量机和线性判别分析等分类器进行了比较，所提出的方法优于这些分类器和最先进的技术。总体而言，这项研究证明了使用智能算法和特征提取技术来提高使用微阵列基因表达数据进行癌症诊断的准确性的潜力。