Data, classification

The basis of classification is supervised learning where a set of known objects that belong unambiguously to certain classes are analyzed. From their features (analytical data) classification rules are obtained by means of relevant properties of the data like dispersion and correlation. 

It is our goal in this book to address the problems, introduced earlier, that arise in a general data reconciliation problem. It is the culmination of several years of research and implementation of data reconciliation aspects in Argentina, the United States, and Australia. It is designed to provide a simple, smooth, and readable account of all aspects involved in data classification and reconciliation, while providing the interested reader with material, problems, and directions for further study. 

Sanchez, M., and Romagnoli, J. (1996). Use of orthogonal transformations in data classification— reconciliation. Comput. Chem. Eng. 20, 483-493. 

Liu A et al (2002) Block principal component analysis with application to gene microarray data classification. Stat Med 21 3465-3474. doi 10.1002/sim.l263... 

Stanley, C. R. (1988). Comparison of data classification procedures in applied geochemistry using Monte Carlo simulation Unpublished Ph.D. Thesis. University of British Columbia, Vancouver. 

Website sphingolipid classifaction list http //www.lipidmaps.org/data/ classification/sp.html... 

A one-level data classification may be insufficient for data characterization. Data structure knowledge may require a more sophisticated analysis. 

It is essential for the clinical implementation of the MRS technology that high sensitivity and specificity are available reproducibly. This requirement was the driving force in the development of the statistical classification strategy (SCS)-based multivariate analysis methods that form the focus of this review. This review aims to summarize the clinical MRS studies reported to date that have included clinical outcomes and/or histopathological assessment of the entire biopsy specimen examined by MRS and where the data have been analysed in recognition of the criteria essential for robust data classification. 

The three-stage data classification strategy has been developed primarily during the MR and pathological assessment of the six separate organs summarized below. The stages were developed either as an improvement of the strategy per... 

Table 2. Summary of MR data classification using a three-stage strategy. Biopsy type (n) Classification Method3... 

In summary, this first report of a classification strategy specifically tailored to the classification of biomedical spectral data shows that reliable and robust classification must satisfy the following criteria adequate data set size proper data reduction proper data classification (balanced training set with cross-validation, e.g. LOO) the use of several classifiers and choice of appropriate consensus classification. 

For data classification, the spectra were partitioned into training and validation (test) sets. The four differently preprocessed sets of H MR brain spectra were subjected to two classification methods LDA and a noise-augmented artificial neural net (NN). All classifier training was cross-validated via the LOO method. The two classifiers (LDA and NN) were used on three-class (E, M and A) data. CCD was then implemented based on stacked generalization.61... 

No. Simulated data Actual class of X y simulated data Classification results Hierarchical K-means simulated clustering atmealing ... 

Actual class of simulated data Classification results of ... 

Lipid Metabolites and Pathways Strategy — http //www.lipidmaps. org/data/classification/fa.html... 

Rao, K.R. and Lakshminarayanan, S. (2007) Partial correlation based variable selection approach for multivariate data classification methods. Chemom. Intell. Lab. Syst., 86, 68-81. 

A basic technique for data classification. Cluster analysis is aimed at grouping items in a set into clusters. The item in each cluster should share some commonalities that justify to group them. The similarity or difference between different items is usually measured by a function of pairs of items. If we measure difference (similarity), the values decrease (increase) with increasing similarity between a pair of items. 

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