Abstract
With the help of multi-view classification technology, the classification performance can be effectively improved. However, the traditional multi-view TSK classification method has the problem of dimension explosion when superimposing the features of multiple views. This paper proposes an innovative multi-view TSK classification method, which makes individual decisions in each view, and then selects the most credible decision results to obtain comprehensive classification results. Compared with other algorithms, our algorithm shows strong competitiveness in accuracy rate. This study also conducts experimental validation on different datasets, proving that this method exhibits good performance in multiple domains. Different views are dynamically integrated by the multi-view decision TSK algorithm by at the evidence level, providing a feasible idea for multi-view TSK classification. A new method of multi-view TSK classification has been developed in this study, which addresses the issue of high dimensionality present in traditional multi-view TSK classification methods. The experimental results provide strong evidence in support of the feasibility and potential for this proposed method of implementation and application.
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Nie, L., Qian, Z., Zhao, Y., Jiang, Y. (2023). A Novel Algorithm to Multi-view TSK Classification Based on the Dirichlet Distribution. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science(), vol 14090. Springer, Singapore. https://doi.org/10.1007/978-981-99-4761-4_47
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DOI: https://doi.org/10.1007/978-981-99-4761-4_47
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