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Kang W, Xiao J, Xue J. Generative knowledge-based transfer learning for few-shot health condition estimation. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-022-00787-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
AbstractIn the field of high-end manufacturing, it is valuable to study few-shot health condition estimation. Although transfer learning and other methods have effectively improved the ability of few-shot learning, they still cannot solve the lack of prior knowledge. In this paper, by combining data enhancement, knowledge reasoning, and transfer learning, a generative knowledge-based transfer learning model is proposed to achieve few-shot health condition estimation. First, with the effectiveness of data enhancement on machine learning, a novel batch monotonic generative adversarial network (BM-GAN) is designed for few-shot health condition data generation, which can solve the problem of insufficient data and generate simulated training data. Second, a generative knowledge-based transfer learning model is proposed with the performance advantages of the belief rule base (BRB) method on few-shot learning, which combines expert knowledge and simulated training data to obtain a generalized BRB model and then fine-tunes the generalized model with real data to obtain a dedicated BRB model. Third, through uniform sampling of NASA lithium battery data and simulating few-shot conditions, the generative transfer-belief rule base (GT-BRB) method proposed in this paper is verified to be feasible for few-shot health condition estimation and improves the estimation accuracy of the BRB method by approximately 17.3%.
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Yang LH, Liu J, Ye FF, Wang YM, Nugent C, Wang H, Martínez L. Highly explainable cumulative belief rule-based system with effective rule-base modeling and inference scheme. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2021.107805] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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An Integrated Deep Learning and Belief Rule-Based Expert System for Visual Sentiment Analysis under Uncertainty. ALGORITHMS 2021. [DOI: 10.3390/a14070213] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Visual sentiment analysis has become more popular than textual ones in various domains for decision-making purposes. On account of this, we develop a visual sentiment analysis system, which can classify image expression. The system classifies images by taking into account six different expressions such as anger, joy, love, surprise, fear, and sadness. In our study, we propose an expert system by integrating a Deep Learning method with a Belief Rule Base (known as the BRB-DL approach) to assess an image’s overall sentiment under uncertainty. This BRB-DL approach includes both the data-driven and knowledge-driven techniques to determine the overall sentiment. Our integrated expert system outperforms the state-of-the-art methods of visual sentiment analysis with promising results. The integrated system can classify images with 86% accuracy. The system can be beneficial to understand the emotional tendency and psychological state of an individual.
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A minimum centre distance rule activation method for extended belief rule-based classification systems. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106214] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Hossain S, Sarma D, Chakma RJ, Alam W, Hoque MM, Sarker IH. A Rule-Based Expert System to Assess Coronary Artery Disease Under Uncertainty. COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE 2020. [DOI: 10.1007/978-981-15-6648-6_12] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Tang X, Wang X, Xiao M, Yung KL, Hu B. Health condition estimation of spacecraft key components using belief rule base. ENTERP INF SYST-UK 2019. [DOI: 10.1080/17517575.2019.1670361] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Xilang Tang
- ATS Lab, Air Force Engineering University, Xi’an, China
| | - Xueqi Wang
- ATS Lab, Air Force Engineering University, Xi’an, China
| | - Mingqing Xiao
- ATS Lab, Air Force Engineering University, Xi’an, China
| | - Kai Leung Yung
- Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hong Kong, China
| | - Bin Hu
- Marketing department, China Mobile Communications Corporation, Xiangtan, China
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Research and development project risk assessment using a belief rule-based system with random subspaces. Knowl Based Syst 2019. [DOI: 10.1016/j.knosys.2019.04.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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