Abstract

Big data analytics is challenged by high dimensionality, redundant attributes, heterogeneous data distributions, and the computational burden of processing large-scale datasets. The current filter, wrapper and matrix factorization approaches typically ignore the relationships among features or converge too early, are not very adaptive, and are not very efficient in searching. To fill this space, this study offers an Intelligent Feature Selection and Large-Scale Data Analytics Framework based on Fuzzy Adaptive Bobcat Optimization Algorithm–Double Deep Q-Network (FABOA–DDQN). In addition to the Bobcat Optimization Algorithm, FABOA introduces triangular fuzzy membership functions to adjust exploration and exploitation based on the diversity of the population, improvement in fitness and the number of iterations. Its wrapper objective takes into account predictive performance, feature-subset compactness and feature redundancy. DDQN is not just a sequential combination, but rather a policy-learning controller: Candidate feature subsets are states, adding or removing features or changing their values is an action, and the reward value is a measure of fitness improvement and feature reduction. The online and target networks alleviate Q-value overestimation and stabilize search decisions. The experimental results were obtained by applying a 70:15:15 training/validation/testing split of a widely used SVM evaluator, 30 independent trials and tested against NNMF, S4NMF, RLNMFAG, DBMD variants, BOA, GA, PSO, WOA, DQN, and DDQN. On Covertype, FABOA–DDQN achieved 98.34% precision, 96.77% recall, 97.55% F1-score, and 97.10% RI. It surpassed DBMD-DLM-BFGS in all four of these measures by as much as 1.95%, 3.44%, 2.69%, and 3.29%, respectively, across all four datasets while bringing the ratio of the selected features down to 31%.

Keywords

Big Data Analytics, Feature Selection, Fuzzy Adaptive Bobcat Optimization Algorithm (Faboa), Double Deep Q Network (Ddqn), Clustering Optimization, Evaluation Metrics,

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