The present investigation concerns methodological and epidemiological aspects of the transferability of artificial neural network-based algorithms, as key-components for classification in decision support systems (DSS).
In analysis of MetS (as defined by NCEP) dichotomizing each MetS component for classification and making a simple count of abnormalities may lose information regarding independent associations.
We also showed that by using Isomap components for supervised classification with k-nearest neighbor (k-NN) and quadratic discriminant analysis (QDA), apoptosis intensity can be predicted for different combinations of TNF, EGF and insulin.
In order to improve the generalization capability of the classifier and facilitate its design [ 19, 20], the feature selection should be performed to reduce the dimensionality of the original feature vectors and retain the useful feature components for movement classification.
By applying the systematic study on the analysis of gene microarray data, we try to study whether feature selection selects proper components for PCA and PLS dimension reduction and whether only the top components are nontrivial for classification.
Structured lenslets are used to extract frequency components of targets for classification, the location and orientation of the sensors are combined to track targets when they move through the space.
Furthermore, we consider not all the initial components are important for classification, subsets should be selected for classification.
Further study of our experiments indicates that not all the top components of PCA and PLS are useful for classification, the tail component also contain discriminative information.
The First Confinement System (FCS) is formed by the ex-vessel mm-wave waveguide components, for which SIC-1 classification requirements apply.
Mutations in KRAS and BRAF genes seem to occur in a mutually exclusive manner, and both are suggested as integral components for an effective molecular classification of colorectal cancer.
