A New Ensemble Learning Method for Multiple Fusion Weighted Evidential Reasoning Rule

Abstract

Ensemble learning, as a kind of method to improve the generalization ability of classifiers, is often used to improve the model effect in the field of deep learning. However, the present ensemble learning methods mostly adopt voting fusion in combining strategies. This strategy has difficulty mining effective information from the classifiers and cannot effectively reflect the relationship between different classifiers. Ensemble learning based on the evidential inference rule (ER rule) can effectively excavate the internal relationships among different classifiers and has a certain interpretability. However, the ER rule depends on the weight distribution of different combination strategies, and the setting of the evidence weight will affect the accuracy and stability of the model. Therefore, this paper proposes a new ensemble learning method based on multiple fusion weighted evidential reasoning rules and constructs an ensemble learning framework for data fusion and decision mapping. This framework takes the evidence weight, confidence, and feature data of each classifier as input and the integration results as output. The weight of evidence was determined by multiple fusion weights of the entropy weight method and order relation method. Finally, the integrated learning process is set up by the ER algorithm. The method proposed in this paper is verified by multiple datasets. Experimental results show that the surface construction model has good performance, and the defects of single weighting instability are greatly improved under the premise of improving the integration effect.

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“…Building Construction cost prediction [80], Low Earth Orbit Satellite Networks [81], Software Development Effort Estimation and defect prediction [82], Tabular and Heterogeneous data [83][84] Healthcare [85], Internet of Medical Things [78], landslide susceptibility mapping [76], Energy Prediction Baggage Threat and Smoke Recognition, Annomaly Detection [17], Cyber Attacks Prediction and weather forecasting [86]. Fig 8 depicts that in this study AdaBoost [87], Decision Tree [88] [89] and Random Forest [49], [? ]…”