Data-driven group decision making for diagnosis of thyroid nodule

Fu, Chao; Chang, Wenjun; Liu, Weiyong; Yang, Shanlin

doi:10.1007/s11432-019-9866-3

Cited by 39 publications

(26 citation statements)

References 70 publications

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“…Many methods have been developed to make decisions with uncertainty, e.g., fuzzy sets [45], belief functions [39], interval number [46] and so on. In applications, it is usually considered that the partial imprecision should be better than an error, since the imprecision can be clarified with other (costly) techniques, but errors may cause serious damage.…”

Section: B Cautious Decision Makingmentioning

confidence: 99%

Combination of Transferable Classification With Multisource Domain Adaptation Based on Evidential Reasoning

Liu

Huang

Zhou

et al. 2021

IEEE Trans. Neural Netw. Learning Syst.

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In applications of domain adaptation, there may exist multiple source domains, which can provide more or less complementary knowledge for pattern classification in the target domain. In order to improve the classification accuracy, a decision-level combination method is proposed for the multisource domain adaptation based on evidential reasoning. The classification results obtained from different source domains usually have different reliabilities/weights, which are calculated according to the domain-consistency. So the multiple classification results are discounted by the corresponding weights under belief functions framework, and then Dempster's rule is employed to combine these discounted results. In order to reduce errors, a neighborhood-based cautious decision making rule is developed to make the class decision depending on the combination result. The object is assigned to a singleton class if its neighborhoods can be (almost) correctly classified. Otherwise, it is cautiously committed to the disjunction of several possible classes. By doing this, we can well characterize the partial imprecision of classification, and reduce the error risk as well. A unified utility value is defined here to reflect the benefit of such classification. This cautious decision-making rule can achieve the maximum unified utility value, because partial imprecision is considered better than an error. Several real data sets are used to test the performance of proposed method, and the experimental results show that our new method can efficiently improve the classification accuracy with respect to other related combination methods.

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Section: B Cautious Decision Makingmentioning

confidence: 99%

Combination of Transferable Classification With Multisource Domain Adaptation Based on Evidential Reasoning

Liu

Huang

Zhou

et al. 2021

IEEE Trans. Neural Netw. Learning Syst.

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show abstract

“…Due to the advantages of multi-sensor setups, in recent years, it has been widely used in fault diagnosis, target positioning, and UAV system control [ 6 , 7 , 8 , 9 , 10 ]. The practical experience shows that comparing with a single-sensor system, multi-sensor systems can significantly enhance the system performance of detection, identification, and fault diagnosis [ 11 , 12 ]; however, due to various uncertainties in the real world, the information obtained by multi-sensor is affected. In addition, due to the influence of the sensor itself, the information obtained by multi-sensor systems may be inaccurate, uncertain, or even be faulty [ 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Conflict Management Method Based on Uncertainty of Evidence and Reinforcement Learning for Multi-Sensor Information Fusion

Huang

Wang

Deng

2021

Entropy

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Dempster–Shafer theory (DST), which is widely used in information fusion, can process uncertain information without prior information; however, when the evidence to combine is highly conflicting, it may lead to counter-intuitive results. Moreover, the existing methods are not strong enough to process real-time and online conflicting evidence. In order to solve the above problems, a novel information fusion method is proposed in this paper. The proposed method combines the uncertainty of evidence and reinforcement learning (RL). Specifically, we consider two uncertainty degrees: the uncertainty of the original basic probability assignment (BPA) and the uncertainty of its negation. Then, Deng entropy is used to measure the uncertainty of BPAs. Two uncertainty degrees are considered as the condition of measuring information quality. Then, the adaptive conflict processing is performed by RL and the combination two uncertainty degrees. The next step is to compute Dempster’s combination rule (DCR) to achieve multi-sensor information fusion. Finally, a decision scheme based on correlation coefficient is used to make the decision. The proposed method not only realizes adaptive conflict evidence management, but also improves the accuracy of multi-sensor information fusion and reduces information loss. Numerical examples verify the effectiveness of the proposed method.

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“…Data-driven analysis methods can be used as objective and rational tools to understand the data and make decisions [15,16]. For example, in the field of life sciences [17], the data-driven approach was used to conduct diagnosis [18,19]. In the field of engineering applications, the data-driven method has been used to obtain information on road lighting infrastructure.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction

Tian

Wang

2020

Applied Sciences

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The prediction of the remaining life of a bearing plays a vital role in reducing the accident-related maintenance costs of machinery and in improving the reliability of machinery and equipment. To predict bearing remaining useful life (RUL), the abilities of statistical characteristics to reflect the bearing degradation state differ, and the single prediction model has low generalization ability and a poor prediction effect. An ensemble robust prediction method is proposed here to predict bearing RUL based on the construction of a bearing degradation indicator set: the initial bearing degradation indicator subsets were constructed using the Fast Correlation-Based Filter with Approximate Markov Blankets (FCBF-AMB) and Maximal Information Coefficient (MIC) selection methods. Through the cross-operation of the obtained subsets, we obtained a set of robust degradation indicators. These selected degradation indicators were fed into the long short-term memory (LSTM) neural network prediction model enhanced by the AdaBoost algorithm. We found through calculation that the average prediction accuracy of the proposed method is 91.40%, 92.04%, and 93.25% at 2100, 2250, and 2400 rpm, respectively. Compared with other methods, the proposed method improves the prediction accuracy by 1.8% to 14.87% at most. Therefore, the method proposed in this paper is more accurate than the other methods in terms of RUL prediction.

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Data-driven group decision making for diagnosis of thyroid nodule

Cited by 39 publications

References 70 publications

Combination of Transferable Classification With Multisource Domain Adaptation Based on Evidential Reasoning

Combination of Transferable Classification With Multisource Domain Adaptation Based on Evidential Reasoning

A Novel Conflict Management Method Based on Uncertainty of Evidence and Reinforcement Learning for Multi-Sensor Information Fusion

An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction

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