Multi-Label classifier based on Kernel Random Vector Functional Link Network
Vikas Chauhan, Aruna Tiwari, Shivvrat Arya · 2020
In this paper, a kernelized version of the random vector functional link network is proposed for multi-label classification. This classifier uses pseudoinverse to find output weights of the network. As pseudoinverse is non-iterative in nature, it requires less fine-tuning to train the network. Kernelization of RVFL makes it robust and stable as no need to tune the number of neuron in the enhancement layer. A threshold function is used with a kernelized random vector functional link network to make it suitable for multi-label learning problems. Experiments performed on three benchmark multi-label datasets bibtex, emotions, and scene shows that proposed classifier outperforms various the existing multi-label classifiers.