Predictive Modeling With an Adaptive Unsupervised Broad Transfer Algorithm

Guokai Liu; Weiming Shen; Liang Gao; Andrew Kusiak

doi:10.1109/TIM.2021.3088496

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Predictive Modeling With an Adaptive Unsupervised Broad Transfer Algorithm

Journal article

Peer reviewed

Predictive Modeling With an Adaptive Unsupervised Broad Transfer Algorithm

Guokai Liu, Weiming Shen, Liang Gao and Andrew Kusiak

IEEE transactions on instrumentation and measurement, Vol.70, pp.1-12

2021

DOI: 10.1109/TIM.2021.3088496

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Abstract

Deep-learning algorithms have produced promising results, however, domain adaptation remains a challenge. In addition, excessive training time and computing resource requirements need to be addressed. Deep-learning algorithms face a domain adaptation issue when the data distribution of a target domain differs from that of the source domain. The emerging concept of broad learning shows potential in addressing the domain adaptation and training time issues. An adaptive unsupervised broad transfer learning (AUBTL) algorithm is proposed to tackle the cross-domain problems. The proposed algorithm utilizes a sparse autoencoder and random orthogonal mapping to extract and augment the feature space. Then, it initializes the weights of a classifier by solving a ridge regression problem. The logit ranking strategy is applied to develop a transfer estimator to evaluate and sample data in the target domain for an adaptive transfer. Based on the sampled data, AUBTL optimizes the hyperparameter space. The performance of the AUBTL algorithm is validated with three benchmark datasets, including 20 transfer tasks. The computational results demonstrated the efficiency and accuracy of the proposed algorithm over other deep-learning algorithms considered in this research.

Adaptation models

Broad learning

deep learning

domain adaptation

Prediction algorithms

predictive modeling

Predictive models

Sparse matrices

Task analysis

Training

Transfer learning

transfer learning (TL)

Details

Title: Subtitle: Predictive Modeling With an Adaptive Unsupervised Broad Transfer Algorithm
Creators: Guokai Liu - Huazhong University of Science and Technology
Weiming Shen - Huazhong University of Science and Technology
Liang Gao - Huazhong University of Science and Technology
Andrew Kusiak - University of Iowa
Resource Type: Journal article
Publication Details: IEEE transactions on instrumentation and measurement, Vol.70, pp.1-12
Publisher: IEEE
DOI: 10.1109/TIM.2021.3088496
ISSN: 0018-9456
eISSN: 1557-9662
Grant note: 201906160078 / China Scholarship Council (CSC) during a research visit of Guokai Liu to the University of Iowa (10.13039/501100004543)
Language: English
Date published: 2021
Academic Unit: Industrial and Systems Engineering; Nursing
Record Identifier: 9984186976102771

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