Enhancing IoT (Internet of Things) feature selection: A two-stage approach via an improved whale optimization algorithm.

Feature selection is a critical task for optimizing system performance and reducing computational overhead in the context of Internet of Things (IoT) applications. This paper presents a two-stage feature selection approach specifically designed for IoT scenarios. In the first stage, a variety of feature dimensionality reduction techniques are employed to significantly reduce the dimensionality of the original feature set by more than 50%. This process results in the creation of a highly effective feature subset, which serves as a solid foundation for subsequent feature selection in the second stage. In the second stage, feature selection is performed on the feature subset by an evolutionary algorithm to obtain high accuracy. Notably, we propose an improved whale optimization algorithm (WOA-HA), which incorporates several improvement factors such as a chaotic Hénon map mechanism (HMM), adaptive coefficient vector (ACV), and a binary operator. To assess the effectiveness of our approach, we compare the performance of WOA-HA with other evolutionary algorithms in terms of feature selection outcomes. Through extensive experiments, our proposed approach achieves an average accuracy of up to 95.5% on Aalto IoT dataset and 98.8% on RT-IoT 2022 dataset, respectively. Meanwhile, the average number of selected features reduced by about 82.5% on Aalto IoT dataset and about 62.3% in the RT-IoT 2022 dataset, respectively. Our proposed approach consistently outperforms other methods and achieves the best performance on most datasets with higher accuracy and fewer features. • Two-stage feature selection approach is proposed for the high-dimensionality of IoT data. • A novel WOA-HA algorithm is proposed in the second stage. • WOA-HA has a special chaotic initial solution and adaptive strategy. • The proposed approach outperforms other algorithms on Aalto IoT dataset and RT-IoT 2022 dataset. • The first stage of experimentation with the Aalto dataset resulted in a reduction of the feature set dimensionality by over 50%. [ABSTRACT FROM AUTHOR]