In the biomedical field, large amounts of biological and clinical data have been accumulated rapidly, which can be analyzed to emphasize the assessment of at-risk patients and improve diagnosis. However, a major challenge encountered associated with biomedical data analysis is the so-called "curse of dimensionality." For this issue, a novel feature selection method based on an improved binary clonal flower pollination algorithm is proposed to eliminate unnecessary features and ensure a highly accurate classification of disease. The absolute balance group strategy and adaptive Gaussian mutation are adopted, which can increase the diversity of the population and improve the search performance. The KNN classifier is used to evaluate the classification accuracy. Extensive experimental results in six, publicly available, high-dimensional, biomedical datasets show that the proposed method can obtain high classification accuracy and outperforms other state-of-the-art methods.

中文翻译：

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基于改进的二进制克隆花授粉算法的高维生物医学数据特征选择新方法。

在生物医学领域，已经迅速积累了大量的生物学和临床数据，可以对其进行分析以强调对风险患者的评估并改善诊断。但是，与生物医学数据分析相关的主要挑战是所谓的“维数诅咒”。针对此问题，提出了一种基于改进的二元克隆花授粉算法的特征选择方法，以消除不必要的特征并确保疾病的高精度分类。采用绝对平衡群策略和自适应高斯变异，可以增加种群的多样性，提高搜索性能。KNN分类器用于评估分类准确性。六种可公开获得的高尺寸，