A Novel Hybrid Sine Cosine-Flower Pollination Algorithm for Optimized Feature Selection

Data serves as the foundation for training and testing machine learning and artificial intelligence models. The most fundamental part of data is its attributes or features. The feature set size changes from one dataset to another. Only the relevant features contribute meaningfully to classification accuracy. The presence of irrelevant features reduces the system’s effectiveness. Classification performance often deteriorates on high-dimensional datasets due to the large search space. Thus, one of the significant obstacles affecting the performance of the learning process in the majority of machine learning and data mining techniques is the dimensionality of the datasets. Feature selection (FS) is an effective preprocessing step in classification tasks. The aim of applying FS is to exclude redundant and unrelated features while retaining the most informative ones to optimize classification capability and compress computational complexity. In this paper, a novel hybrid binary metaheuristic algorithm, termed hSC-FPA, is proposed by hybridizing the Flower Pollination Algorithm (FPA) and the Sine Cosine Algorithm (SCA). Hybridization controls the exploration capacity of SCA and the exploitation behavior of FPA to maintain a balanced search process. SCA guides the global search in the early iterations, while FPA’s local pollination refines promising solutions in later stages. A binary conversion mechanism using a threshold function is implemented to handle the discrete nature of the feature selection problem. The functionality of the proposed hSC-FPA is authenticated on fourteen standard datasets from the UCI repository using the K-Nearest Neighbors (K-NN) classifier. Experimental results are benchmarked against the standalone SCA and FPA algorithms. The hSC-FPA consistently achieves higher classification accuracy, selects a more compact feature subset, and demonstrates superior convergence behavior. These findings support the stability and outperformance of the hybrid feature selection method presented.