Longzhi Yang

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Fuzzy interpolative reasoning strengthens the power of fuzzy inference by enhancing the robustness of fuzzy systems and reducing systems complexity. However, after a series of interpolations, it is possible that multiple object values for a common variable are inferred, leading to inconsistency in interpolated results. Such inconsistencies may result from(More)
Fuzzy interpolative reasoning has been extensively studied due to its ability to enhance the robustness of fuzzy systems and to reduce system complexity. However, during the interpolation process, it is possible that multiple object values for a common variable are inferred which may lead to inconsistency in interpolated results. Such inconsistencies may(More)
Quality assessment (QA) requires high levels of domain-specific experience and knowledge. QA tasks for toxicological data are usually performed by human experts manually, although a number of quality evaluation schemes have been proposed in the literature. For instance, the most widely utilised Klimisch scheme1 defines four data quality categories in order(More)
Along with the rapid development of data storing and sharing techniques in terms of both hardware and software, multiple data instances scattered across multiple databases may be available to support one single task, and then making choices of data are necessary from time to time. Research has been conducted on quality or reliability evaluation for(More)
Due to the advance of modern computing technology, decisions can be made based on all the existing related data instances scattered across multiple data storages, such that available information has been entirely taken into consideration. Particularly in the predictive toxicology domain, because of the heterogeneity of data sources, multiple data instances(More)
Adaptive fuzzy interpolation strengthens the potential of fuzzy interpolative reasoning. It views interpolation procedures as artificially created system components, and identifies all possible sets of faulty components that may each have led to all detected contradictory results. From this, a modification procedure takes place, which tries to modify each(More)
Adaptive fuzzy interpolation strengthens the potential of fuzzy interpolative reasoning owning to its efficient identification and correction of defective interpolated rules during the interpolation process [11]. This approach assumes that: i) two closest adjacent rules which flank the observation or a previously inferred result are always available; ii)(More)
Ensembles of classifiers proved potential in getting higher accuracy compared to a single classifier. High diversity in an ensemble may improve the performance results significantly. We propose an ensemble approach which has diversity calculated using disagreement measure of classification output. A CRS (Classifier Ranking System) is introduced for the(More)