Feature Selection and Classification of Microarray Datasets Based on an Improved Binary Harris Hawks Optimization Algorithm

High-dimensional microarray datasets are prone to the “curse of dimensionality” due to feature redundancy, which impairs the performance of machine learning models, and feature selection is the key to addressing this issue. This study proposes an Improved Binary Harris Hawks Optimization algorithm (IBHHO) for feature selection in high-dimensional microarray data. Core innovations comprise: i) a hybrid filter-wrapper framework integrating a filter method (ReliefF), a wrapper method (HHO) and a classifier (SVM) to simultaneously optimize ReliefF parameters, SVM hyperparameters, and feature subsets; ii) a differentiated exploration–exploitation strategy leveraging HHO’s two-stage behavior (global parameter optimization during exploration; feature refinement and local parameter tuning during exploitation); and iii) an elite feature guidance strategy that reduces redundant exploration and accelerates convergence via fixed key-feature anchor points. Experiments conducted on eight public microarray datasets demonstrate that IBHHO reduces feature counts while improving classification accuracy, achieving comprehensive performance superior to benchmark algorithms. Consequently, IBHHO offers an efficient feature selection frame-work for high-dimensional biomedical data analysis.

@article{LI2025,
  title     = {Feature Selection and Classification of Microarray Datasets Based on an Improved Binary Harris Hawks Optimization Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Guoxia LI and Wen SHI and Jingyu ZHANG and Zhixia GU and Jixiang XU and Yueyue LI and Yaxing SUN},
  doi       = {10.14569/IJACSA.2025.0160884},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160884}
}