Exact(1)
Tables 2, 3, 4, 5, 6 and 7 present the performance achieved by each classifier for each Enron dataset.
Similar(59)
The genes selected by each classifier are summarized alongside p-values for the log-rank tests for differences between the survival rates of dacetuzumab sensitive, semi-sensitive, and resistant patients, and for the two-way difference between the survival rates of dacetuzumab sensitive and resistant patients only.
The list is sorted according to the confidence score given by the classifier for each prediction.
The visualization is exclusively based on the probability estimate provided by the classifier for each class value.
The True Positive rate achieved by each type of classifier for the Primary Screen datasets.
The True Positive rate achieved by each type of classifier for the Mixed Primary Screen/Confirmatory Screen datasets.
First, the LDF for a position is estimated by the classifier for PAS-strong sites; if it is not classified as PAS-strong site, the LDF for that position is estimated by the classifier for PAS-weak sites; otherwise it is estimated by the classifier for PAS-less sites.
The training set threshold is adjusted according to data set GSE4475 and the class probability given to each sample by the original classifier; for example, training set Th=0.9 means only include the samples that have a confidence over 0.9 in GSE4475 to train the classifier, and Strict and Wide refer to the strict and wide definition used previously.
The feature descriptors, obtained by texture analysis, are then utilized by the classifier for the diagnosis of motor faults.
In Section 3.2 we point out that reasonable decisions cannot be made by the classifier for very dark and very bright colors.
Motivated by the fact that the responses generated by a classifier for two objects can highly reflect their semantic similarity, we attempt to exploit a set of classifiers to construct feature extraction method.
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