Methylation-based classifiers were designed to distinguish between ten pairs of groups: ALL against reference samples, female against male, and each of the eight subtypes T-ALL, HeH, t(12 21), 11q23/ MLL, t(1 19), dic(9 20), t(9 22), and iAMP21 against a background of the other ALL subtypes.
Finally, boosting-based classifier is designed based on DSEPs.
The classifiers were designed on the training data set.
Two different classifiers are designed based on the two different representation models of the text documents.
Traditional classifiers are designed based on global or local structural risk and their designs do not take both of them into account simultaneously.
Such classifiers are designed based on cross-correlation in the frequency domain between the input matrix and the input weights of neural networks.
In bagging, component classifiers are designed based on bootstrap sets, each of which contain on average of the original sample set.
In [1], two fuzzy classifiers and a Bayesian classifier were designed for throat polyp detection based on patient vowel voices/a:/and/i:/.
Two fuzzy classifiers and a Bayesian classifier were designed for throat polyp detection based on patient vowel voices /a:/ and /i:/.
Within the same group, the MLPNN based classifier was more accurate than the LR based classifier.
In the classifier module, several supervised classifiers, such as multilayer perceptron neural network, radial basis function and multi-class support vector machine based classifier are investigated.
