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In the case of concordance between NSCs and differentiated astrocytes or neurons, a +/− 10% standard error binary classifier was used, based on the estimate that up to 10% of genes would show concordance by chance alone.
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A binary classifier is used to analyze these results in the same way as the mapped TFs are analyzed in the previous subsection.
The binary classifiers were used to evaluate the number of wholly disordered proteins in a given proteome.
A simple linear classifier is used after LDA for binary classification.
The PNN classifier is used as an effective classifier for the classification of the PQDs.
If neural network decision is for more than one class as obtained from the first step, binary neural network classifiers are used in the second step for crisp classification between two classes.
The performance of a binary classifier is assessed using a test set ( S_{test} ) that consists of both positive (( S_{test}^ )) and negative (( S_{test}^ )) samples not used for model building [25].
For 119 iterations, one sample was left out, unit binary classifiers of an appropriate combination were constructed using data from the remaining 118 samples, each binary classifier was run, and the binary outputs were summated and classified.
The prediction algorithm is a binary classifier which is used to place edges in a graph.
The extracted features are then passed for classification via ECOC approach where binary classifiers are coded using Sparse random coding design and decoded using Hamming distance.
Each binary classifier is a standard SVM with a Gaussian kernel (we used libsvm (Chang and Lin, 2001) for our experiments).
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