Interpretable Clustering Using Dempster-Shafer Theory
This study presents DSClustering, a novel algorithm that merges clustering validity with interpretability using the Dempster-Shafer theory. Traditional clustering methods like K-means, DBSCAN, and agglomerative clustering, while widely used for their efficiency and accuracy, often fall short in transparency, creating barriers in critical fields such as
Aram Adamyan +4 more
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A New Belief Entropy in Dempster-Shafer Theory Based on Basic Probability Assignment and the Frame of Discernment. [PDF]
Li J, Pan Q.
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Incomplete Information Management Using an Improved Belief Entropy in Dempster-Shafer Evidence Theory. [PDF]
Yang B, Gan D, Tang Y, Lei Y.
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Automatic Updates of Transition Potential Matrices in Dempster-Shafer Networks Based on Evidence Inputs. [PDF]
Dunham J, Johnson E, Feron E, German B.
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Standing-Posture Recognition in Human-Robot Collaboration Based on Deep Learning and the Dempster-Shafer Evidence Theory. [PDF]
Li G, Liu Z, Cai L, Yan J.
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Dempster-Shafer teorisinin değerlendirme problemine uygulanması
SUMMARY APPLICATION OF DEMPSTER-SHAFER THEORY TO AN EVALUATION PROBLEM To make a final conclusion in decision making problems with multi crite ria and multi experts can be quite difficult. Because the expert's evaluation of product, person,... etc., based on multi criteria may involve uncertainti es.
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Restricted Network Reconstruction from Time Series via Dempster-Shafer Evidence Theory. [PDF]
Zhang C, Xian Y, Yuan X, Li M, Zhang Q.
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An emotion-oriented product evaluation model based on BP neural network and improved dempster-shafer theory. [PDF]
Wang K, Li K, Jiang X, Chen C.
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Efficient Deep Wavelet Gaussian Markov Dempster-Shafer Network-Based Spectrum Sensing at Very Low SNR in Cognitive Radio Networks. [PDF]
Jatti S, Tyagi A.
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