Abstract
Data with high uncertainty and ambiguity is challenging for the classification task. EKNN is a popular evidence theory-based classification method developed for handling uncertainty data. However, as a distance-based technique, it also suffers from the problem of high dimensionality as well as performs ineffectively with mixed distribution data where closed data points originated from different classes. In this paper, we propose a new classification method that can softly classify new data upon each separate cluster which can remedy the overlapping data problem. Moreover, pieces of evidence induced from the trained clusters are combined using Dempster’s combination rule to yield the final predicted class. By defining the mass function of evidence with the weight factor based on the distance between new data points and clusters’ centers, it helps reduce the computational complexity which is also a problem in distance-based k-NN alike methods. The classification experiment conducted on various real data and popular classifiers has shown that the proposed technique has the results comparable to state-of-the-art methods.
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This research was supported in part by the US Office of Naval Research Global under Grant no. N62909-19-1-2031.
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Nguyen, TP., Huynh, VN. (2020). A New Classification Technique Based on the Combination of Inner Evidence. In: Huynh, VN., Entani, T., Jeenanunta, C., Inuiguchi, M., Yenradee, P. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2020. Lecture Notes in Computer Science(), vol 12482. Springer, Cham. https://doi.org/10.1007/978-3-030-62509-2_15
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