Randomized neural networks for multilabel classification

doi:10.1016/j.asoc.2021.108184

2022-01-01

PRE

Vikas Chauhan *

Aruna Tiwari

DOI：10.1016/j.asoc.2021.108184

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摘要

En 中文

Multilabel classification is a supervised learning problem in which input instances belong to multiple output labels. In this paper, we propose noniterative randomization-based neural networks for multilabel classification. These multilabel neural networks are named as Multilabel Random Vector Functional Link Network (ML-RVFL), Multilabel Kernelized Random Vector Functional Link Network (ML-KRVFL), Multilabel Broad Learning System (ML-BLS), and Multilabel Fuzzy Broad Learning System (ML-FBLS). The output weights of these neural networks are computed using pseudoinverse. At the output layer, multilabel classification is performed by using an adaptive threshold function. The computation of output weights using pseudoinverse retains the faster computation power of these algorithms compared to iterative learning algorithms. The adaptive threshold function used in the proposed approach can consider the correlation among the output labels and the whole dataset for threshold computation. Five multilabel evaluation metrics evaluate the proposed multilabel neural networks on 12 benchmark datasets of various domains such as text, image, and genomics. The MLKRVFL provides the overall best Friedman rankings on five evaluation metrics followed by ML-RVFL, ML-FBLS, and ML-BLS, respectively. Based on the experimentation results, the proposed ML-KRVFL, ML-RVFL, ML-FBLS, and ML-BLS perform better than other relevant multilabel approaches in the mentioned order.The proposed approaches are faster than other state-of-the-art iterative approaches and noniterative approaches in terms of running time. (C) 2021 Elsevier B.V. All rights reserved.

Keyword:

Multilabel classification

Multilabel Randomized Neural Networks

Random Vector Functional Link Network

Kernel Random Vector Functional Link Network

Noniterative learning

Broad Learning System

Fuzzy Broad Learning System