Binary plant rhizome growth-based optimization algorithm: an efficient high-dimensional feature selection approach

Abstract

Feature selection is a pivotal research area within machine learning, tasked with pinpointing the essential subset of features from a broad array that critically influences a model’s predictive capabilities. This process enhances model precision and drastically lowers the computational demands associated with training and predicting. Consequently, more advanced optimization techniques are employed to address the challenge of feature selection. This paper introduces an innovative intelligent optimization algorithm, the Plant Root Growth Optimization (PRGO) algorithm, inspired by the structure of plant rhizomes and the way they absorb nutrients.In the algorithm, the plant rhizomes are divided into two categories, the taproot and the fibrous root.the growth process of the taproot plants is associated with the global exploration search, and the growth process of the fibrous root plants relates to the local exploitation search.The global asymptotic convergence of the algorithm is proved by applying Markov’s correlation theory, and simulation results using CEC2014 and CEC2017 test sets show that the proposed algorithm has excellent performance.Moreover, a binary variant of this algorithm (BPRGO) has been specifically crafted in this research to tackle the complexities of high-dimensional feature selection issues. The algorithm was compared to eight well-known feature selection methods and its performance was evaluated using a variety of evaluation metrics on 16 high-dimensional datasets from the Arizona State University feature selection library. and the performance of the proposed algorithm was evaluated through feature subset size, classification accuracy, fitness value, and F1-score. The experimental results show that BPRGO achieves the best performance, which has stronger feature reduction ability and achieves better overall performance on most datasets. BPRGO can obtain extremely smaller feature subsets while maintaining much higher classification accuracy, and satisfactory F1-score.