Abstract
Due to its efficiency to handle uncertain information, Dempster–Shafer evidence theory has become the most important tool in many information fusion systems. However, how to determine basic probability assignment, which is the first step in evidence theory, is still an open issue. In this article, a new method integrating interval number theory and k-means++ cluster method is proposed to determine basic probability assignment. At first, k-means++ clustering method is used to calculate lower and upper bound values of interval number with training data. Then, the differentiation degree based on distance and similarity of interval number between the test sample and constructed models are defined to generate basic probability assignment. Finally, Dempster’s combination rule is used to combine multiple basic probability assignments to get the final basic probability assignment. The experiments on Iris data set that is widely used in classification problem illustrated that the proposed method is effective in determining basic probability assignment and classification problem, and the proposed method shows more accurate results in which the classification accuracy reaches 96.7%.
Highlights
Multi-source information fusion technology plays a significant role in real applications, such as classification problem,[1,2,3,4,5,6] fault diagnosis,[7,8,9] medical diagnosis,[10] risk and reliability analysis,[11] decision-making,[12,13] tracking problem,[14,15] and online estimation of batteries state-ofcharge.[16]
Based on the analysis described above, an improved method that constructs the interval number model using k-means++ cluster method is proposed
Since the interval number is available with fewer data from the information source, in the real application, it is easy to apply this method in many engineering applications to accomplish multisource data fusion and classification
Summary
Information fusion is a process to combine information from the multi-source of same object or scene to obtain more complex, reliable, and accurate information. Multi-source information fusion technology plays a significant role in real applications, such as classification problem,[1,2,3,4,5,6] fault diagnosis,[7,8,9] medical diagnosis,[10] risk and reliability analysis,[11] decision-making,[12,13] tracking problem,[14,15] and online estimation of batteries state-ofcharge.[16] There are many methods to analyze fused data from multi-sources including Dempster–Shafer evidence theory (DS theory),[1,2,3,4] principal component analysis (PCA),[17,18] independent component analysis (ICA),[19,20] and Z-number.[21,22,23] As an effective tool,[24] DS theory has been widely used especially for classification problem.[25,26,27] Compared with ICA and PCA in combination with another kind of approaches, Dempster’s combination rule can fuse multi-source information without depending on prior information[28] and it has tremendous advantages in uncertainty modeling and evidence. How to determine BPA is still an open issue and there is no general method
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