Z
Zhun-ga Liu
Researcher at Northwestern Polytechnical University
Publications - 101
Citations - 2738
Zhun-ga Liu is an academic researcher from Northwestern Polytechnical University. The author has contributed to research in topics: Computer science & Cluster analysis. The author has an hindex of 21, co-authored 78 publications receiving 2003 citations. Previous affiliations of Zhun-ga Liu include École nationale supérieure des télécommunications de Bretagne.
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Classifier Fusion With Contextual Reliability Evaluation
TL;DR: The experimental results show that CF-CRE can produce substantially higher accuracy than other fusion methods in general and is robust to the changes of the number of nearest neighbors chosen for estimating the reliability matrix, which is appealing for the applications.
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Combination of Classifiers With Optimal Weight Based on Evidential Reasoning
TL;DR: A new weighted classifier combination method is proposed based on ER to enhance the classification accuracy and is demonstrated with various real datasets from UCI repository, and its performances are compared with those of other classical methods.
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Evidence Combination Based on Credal Belief Redistribution for Pattern Classification
TL;DR: The rationale of CBR consists of transferring belief from one class not just to other classes, but also to the associated disjunctions of classes (i.e., meta-classes) to reduce uncertainty and further improve classification accuracy.
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Change Detection in Heterogenous Remote Sensing Images via Homogeneous Pixel Transformation
TL;DR: A spatial-neighbor-based noise filter is developed to further reduce the false alarms and missing detections using belief functions theory and to improve the robustness of detection with respect to the noise and heterogeneousness (modality difference) of images.
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Adaptive imputation of missing values for incomplete pattern classification
TL;DR: The credal classification captures well the uncertainty and imprecision of classification, and reduces effectively the rate of misclassifications thanks to the introduction of meta-classes.