An open issue of the Dempster combination rule is the conflicting management, which is very important in multisource data fusion, such as group decision making and target recognition. To address this issue, an improved method to generate basic probability assignment is presented. Then, a new combination method to assign the conflicting mass function without the normalization is proposed to handle a highly conflicting environment. Compared with other methods, this proposed method is convenient in computing and has better accuracy to predict potential possibilities especially when disposing of extreme status. Some numerical examples and real benchmark data collected in UCI database are illustrated to verify the validity and rationality of the proposed method.
In real life, lots of information merges from time to time. To appropriately describe the actual situations, lots of theories have been proposed. Among them, Dempster-Shafer evidence theory is a very useful tool in managing uncertain information. To better adapt to complex situations of open world, a generalized evidence theory is designed. However, everything occurs in sequence and owns some underlying relationships with each other. In order to further embody the details of information and better conforms to situations of real world, a Markov model is introduced into the generalized evidence theory which helps extract complete information volume from evidence provided. Besides, some numerical examples is offered to verify the correctness and rationality of the proposed method.
The construction cost index is an important indicator in the construction industry. Predicting CCI has great practical significance. This paper combines information fusion with machine learning, and proposes a Multifeature Fusion framework for time series forecasting. MFF uses a sliding window algorithm and proposes a function sequence to convert the time sequence into a feature sequence for information fusion. MFF replaces the traditional information method with machine learning to achieve information fusion, which greatly improves the CCI prediction effect. MFF is of great significance to CCI and time series forecasting.
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