A Nature‐Inspired Framework for Dimensionality Reduction and Cancer Diagnosis From Gene Expression Profiles

High‐dimensional gene expression datasets pose significant challenges for cancer classification due to the presence of redundant and irrelevant features. To address this issue, we propose a hybrid framework that integrates the flower pollination algorithm (FPA) with support vector machines (SVM) for effective feature selection and classification. The FPA, inspired by the global and local pollination processes of flowering plants, is adapted into a binary variant using a sigmoid transfer function to select informative subsets of genes. The objective function balances classification accuracy with feature subset sparsity, thereby reducing dimensionality while preserving discriminative power. The selected gene subsets are subsequently evaluated using SVM, which provides robust classification in small‐sample, high‐dimensional scenarios. The proposed FPA‐SVM framework was tested on multiple benchmark cancer datasets, including colon tumor, CNS, ALL‐AML, breast cancer, lung cancer, ovarian cancer, lymphoma, MLL, and SRBCT. Experimental results demonstrate superior performance, with accuracy levels exceeding 98% for most binary‐class datasets and competitive results for multiclass datasets, achieving up to 88.3% accuracy. These findings highlight the effectiveness of the proposed method in enhancing cancer classification, reducing dimensionality, and identifying potential biomarkers for precision medicine.