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Unsupervised elimination

With the exception of those variables having zero variance (which pick themselves), the decision about which variables to eliminate/include and the method by which this is done depends on several factors. The two most important factors are whether the dataset consists of two blocks of variables, a response block (Y) and a descriptor/predictor block (X), and whether the purpose of the analysis is to predict/describe values for one or more of the response variables from a model relating the variables in the two blocks. If this result is indeed the aim of the analysis, then it seems reasonable that the choice of variables to be included should depend, to some extent, on the response variable or variables being modeled. This approach is referred to as supervised variable selection. On the other hand, if the variable set consists of only one block of variables, the choice of variables in any analysis will be done with what are referred to as unsupervised variable selection. [Pg.307]

The need to reduce the number of variables was recognized at about the same time that quantum mechanically derived descriptors proliferated in QSAR. An early procedure, designed to eliminate the smallest number [Pg.307]

The process is cyclical and is continued until the user has reduced the dataset to a suitable size or until no pairs of variables remain with a correlation greater than the user-defined limit. One of the features of this procedure is that the algorithm suggests variables to eliminate. The criterion for judging variables for elimination is how close their distribution is to normal based on skewness and kurtosis. The less normal of the pair is su ested for removal. The eventual use of the variables in any given dataset may not depend on normality however, in the absence of any other criteria for elimination, we believe that normality of their distribution is a reasonable criterion for selection. [Pg.308]

as presently implemented, requires no user intervention other than the choice of standard deviation and correlation coefficient limits. CORCHOP, on the other hand, was designed to be an interactive process that demands user decisions, and it can be tedious for large datasets. UFS is available from the website of the Centre for Molecular Design at the University of Portsmouth (www.cmd.port.ac.uk). [Pg.308]


Whitley DC, Ford MG, Livingstone DJ. Unsupervised forward selection a method for eliminating redundant variables. J Chem Inf Comput Sci 2000 40 1160-8. [Pg.489]

There is now a general awareness of the necessity and benefits of synthetic chemicals and the need for measures to ensure that the potential dangers of a minority of chemicals are detected and, as far as possible, eliminated from the environment. However, the insidious nature of low level, long term exposures means that the inheritance of some decades of largely unsupervised, wide spread distribution of thousands of chemicals may yet be seen in morbidities or mortalities. Thus, there is now the problem of how to identify adverse effects of chemicals which are already in use. In order to derive a logical approach to solving this problem, it is important to understand the historical development... [Pg.459]

Unsupervised Forward Selection A Method for Eliminating Redundant Variables. [Pg.344]

N/A MDDR database Unsupervised single-point transformations based on MCS elimination, subject to similarity prefiltering... [Pg.106]

In unsupervised cases (only an X-matrix is available) a simple criterion for selecting features is the variance. Features with a low variance are considered to possess less information and are eliminated. [Pg.350]


See other pages where Unsupervised elimination is mentioned: [Pg.307]    [Pg.307]    [Pg.455]    [Pg.307]    [Pg.307]    [Pg.455]    [Pg.183]    [Pg.167]    [Pg.169]    [Pg.180]    [Pg.1198]    [Pg.230]    [Pg.291]    [Pg.308]   
See also in sourсe #XX -- [ Pg.307 ]




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