Microarray technology aids in monitoring simultaneous gene expressions of human cells for diagnosis and treatment of infectious diseases in a single experiment. The microarray data are of high dimension with a large number of genes and fewer samples. Highly informative features and appropriate classifiers are necessary to increase the accuracy of disease classification. In this paper, a publicly available colon cancer dataset is analyzed for classification. At first, Power Spectral Density (PSD) technique is employed to obtain the reduced gene features and after that, they are classified using various types of classifiers for colon cancer classification. To improve the classification accuracy, the hybridized evolutionary classifiers, namely, Artificial Bee Colony-Firefly (ABC-Firefly), Artificial Bee Colony-Particle Swarm Optimization (ABC-PSO), Particle Swarm Optimization-Firefly (PSO-Firefly) are derived from the conventional evolutionary classifiers, namely, Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Firefly classifiers. The performance of the three hybridized evolutionary algorithms is compared with the conventional classifiers. Additionally, the comparison was also performed on the conventional and hybridized evolutionary algorithms using Expectation Maximization (EM) and cascaded Power Spectral Density-Expectation Maximization (PSD-EM) dimensionality reduction techniques. Although there are many classifiers for colon classification, it is wise to use PSD features with a Hybrid ABC-PSO classifier for enhanced performance. The experimental results reveal that the Hybrid ABC-PSO classifier with PSD features achieves 98.96% for colon cancer samples and 100% accuracy for colon normal samples when compared with the other classifiers reported in the literature.

中文翻译：

---

提高混合进化算法在微阵列结肠数据分类中的性能

微阵列技术有助于在单个实验中监测人类细胞的同步基因表达，以诊断和治疗传染病。微阵列数据是高维的，基因多，样本少。需要高度信息化的特征和适当的分类器来提高疾病分类的准确性。在本文中，分析了一个公开可用的结肠癌数据集以进行分类。首先，使用功率谱密度（PSD）技术获得减少的基因特征，然后使用各种类型的分类器对它们进行分类以进行结肠癌分类。为了提高分类精度，混合进化分类器，即人工蜂群-萤火虫（ABC-Firefly）、人工蜂群-粒子群优化（ABC-PSO）、粒子群优化-萤火虫（PSO-Firefly）衍生自传统的进化分类器，即人工蜂群（ABC）、粒子群优化（PSO）和萤火虫分类器。将三种混合进化算法的性能与传统分类器进行了比较。此外，还使用期望最大化 (EM) 和级联功率谱密度-期望最大化 (PSD-EM) 降维技术对传统和混合进化算法进行了比较。尽管有许多用于冒号分类的分类器，但明智的做法是将 PSD 特征与 Hybrid ABC-PSO 分类器结合使用以提高性能。实验结果表明，具有PSD特征的Hybrid ABC-PSO分类器达到了98。