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Addition of F-FDG PET improves classification accuracy of IHC to a significant yet minor degree.
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Its principal objective is to improve classification accuracy of fuzzy models.
Trying to improve classification accuracy of M-NSA we propose a new version of this algorithm, called MINSA, where refinement of receptors set is applied.
To exploit the information redundancy that exists among the huge number of variables and improve classification accuracy of microarray data, we propose a gene selection method, Recursive Feature Addition (RFA), which is based on supervised learning and similarity measures.
Considering this reason for low classification accuracy of cross-experiment search, different criteria to evaluate similarity score according to platform type could improve classification accuracy of cross-experiment search.
Using a large number of simulations on benchmark data sets, we show that the proposed CIR significantly improves classification accuracy without compromising interpretability of the fuzzy classifier.
To improve classification accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem.
The use of fractal and multifractal features improved classification accuracy and reduced the influence of signal intensity variations when compared to textural features.
Machine learning-based predictive models constructed upon gene expression profiling have already shown the benefits of using pathway activities as features in terms of improved classification accuracy, compared to those models that consider merely individual gene expression levels [ 61].
This step is important because it can improve classification accuracy while reducing the number of attributes at the same time.
However, combining texture descriptors to improve classification accuracy is not within the scope of this article.
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