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Hence, we propose an unsupervised algorithm aiming at constructing a classifier based on self-paced learning.
We achieved this aim by constructing a classifier based on a nearest neighbor algorithm (NNA), a widely used machine learning approach [58], [59].
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This paper proposes a method called layered genetic programming (LAGEP) to construct a classifier based on multi-population genetic programming (MGP).
As a final step of our algorithm, we constructed a classifier based on the available features, which outputs a posterior probability p e t, q of a compound q being transformed by transformation t e.
This is because the goal of traditional classification modeling is to construct a function (or a classifier) based on the properties of training data so as to make as few errors as possible when being used to predict the class membership of new samples [ 2].
Although this probeset could be used to construct a classifier with a reasonably large margin, the presence of a sample of normal tissue with expression close to the cancer group drastically reduces the specificity of a classifier based upon this probeset.
(A ) Classifier based on cilia siRNA screen features only.
The aim of this study was to construct a new classifier based on the routine clinical markers for the prediction of HBV-induced LC.
Furthermore, it was not possible to construct a prognostic classifier based on the numbers in this study and so any validation cohort would need to be larger to enable this.
The first step in constructing a classifier is to choose the features that the classier uses.
a Cross-validation performance of PCM model constructed by RF classifier based on four different protein descriptors.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com