Broad Learning System Based on Fractional Feature Optimization

Broad learning system (BLS) have demonstrated excellent performance in terms of both speed and accuracy in tasks such as image classification. In BLS, the feature nodes predominantly utilize linear features, and sparse representation is mainly employed in the feature optimization component. The robustness of these features to different data needs to be improved. Although there are many improved algorithms for BLS in feature optimization, there is no improvement based on fractional calculus at present. This article proposes BLS-FC, a novel data classification and regression method that can seamlessly combine BLS and fractional calculation. Fractional calculus describes the properties of data between integer orders and has memory properties. Fractional Fourier transform (Frft) also has time domain and frequency domain information. First, Frft is added to the broad learning feature node extraction to enrich the node features, which is called BLS-Frft. Second, fractional calculus is integrated into the BLS-Frft sparse representation feature optimization, and the feature representation capability is enhanced by fractional differential memory. This part is called BLS-FS. Finally, in order to solve the problem of unstable features of random fractional order subspaces, a fractional order multiscale feature interaction based on BLS-Frft is proposed, which is called BLS-MF. Experimental results across various classification and regression datasets demonstrate the superior performance of the proposed method.