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The number of subzones was determined using fuzzy performance index and normalized classification entropy to optimize the classification algorithm.
Traditionally, the codebook generation methods adopted in the BoW techniques are designed to minimize the quantization error, rather than optimize the classification accuracy.
Through comprehensive application of various methods, we integrated the most effective basic classifiers so as to optimize the classification results.
Subsequently, supervised algorithms such as orthogonal partial least square discrimination analysis (OPLS-DA) are then applied to optimize the classification and extract potential biomarkers for each class.
Moreover, we combine the two groups to perform a feature selection to estimate the structural features with the highest classification ability in order to optimize the classification performance.
We used the Radial Basis Function kernel, and tuned the cost (c) and gamma parameters to optimize the classification performance on the training dataset.
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Using mutual information-based feature selection, we identify 5 key features optimizing the classification of MCI converters.
For supervised learning, through a well designed objective function, the backpropagation strategy directly optimizes the classification results in training dataset by refining the parameter space.
Securing commonality between subjects, e.g., in intervention studies, while still optimizing the classification performance of an analysis of exposure variation is a challenge for further research.
According to the Supplementary Fig. S1, the first threshold optimizes the extraction of reliable candidates for novel DNA/RNA-binding proteins (SE = 52.2%, SP = 96.3%, ACC = 81.8%, and PPV = 87.2%) and the second threshold optimizes the classification performance of CO + PD score (SE = 82.2%, SP = 80%, ACC = 80.7%, and PPV = 66.8%).
We adopted the cutoff values established by Sasson et al. [ 13] to reduce false positives and to optimize the diagnostic classification of the BAP.
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