Svmda

Purpose

SVMDA Support Vector Machine (LIBSVM) for classification.

Synopsis

model = svmda(x,y,options); %identifies model (calibration step)

pred = svmda(x,model,options); %makes predictions with a new X-block

pred = svmda(x,y,model,options); %performs a "test" call with a new X-block and known y-values

Description

SVMDA performs calibration and application of Support Vector Machine (SVM) classification models. (Please see the svm function for support vector machine regression problems). These are non-linear models which can be used for classification problems. The model consists of a number of support vectors (essentially samples selected from the calibration set) and non-linear model coefficients which define the non-linear mapping of variables in the input x-block to allow prediction of the classification as passed in either as the classes field of the x-block or in a y-block which contains numerical classes. It is recommended that regression be done through the svm function.

Svmda is implemented using the LIBSVM package which provides both cost-support vector regression (C-SVC) and nu-support vector regression (nu-SVC). Linear and Gaussian Radial Basis Function kernel types are supported by this function.

Note: Calling svmda with no inputs starts the graphical user interface (GUI) for this analysis method.

Inputs

• x = X-block (predictor block) class "double" or "dataset",
• y = Y-block (predicted block) class "double" or "dataset",
• model = previously generated model (when applying model to new data).

Outputs

• model = a standard model structure model with the following fields (see MODELSTRUCT):
  • modeltype: 'SVM',
  • datasource: structure array with information about input data,
  • date: date of creation,
  • time: time of creation,
  • info: additional model information,
  • pred: 2 element cell array with
    • model predictions for each input block (when options.blockdetail='normal' x-block predictions are not saved and this will be an empty array)
  • detail: sub-structure with additional model details and results, including:
    • model.detail.svm.model: Matlab version of the libsvm svm_model (Java)
    • model.detail.svm.cvscan: results of CV parameter scan
    • model.detail.svm.outlier: results of outlier detection (one-class svm)

• pred a structure, similar to model for the new data.

Options

options = a structure array with the following fields:

• display: [ 'off' | {'on'} ], governs level of display to command window,
• plots [ 'none' | {'final'} ], governs level of plotting,
• preprocessing: {[]} preprocessing structures for x block (see PREPROCESS). NOTE that y-block preprocessing is NOT used with SVMs. Any y-preprocessing will be ignored.
• blockdetails: [ {'standard'} | 'all' ], extent of predictions and residuals included in model, 'standard' = only y-block, 'all' x- and y-blocks.
• algorithm: [ 'libsvm' ] algorithm to use. libsvm is default and currently only option.
• kerneltype: [ 'linear' | {'rbf'} ], SVM kernel to use. 'rbf' is default.
• svmtype: [ {'c-svc'} | 'nu-svc' ] Type of SVM to apply. The default is 'c-svc' for classification.
• probabilityestimates: [0| {1} ], whether to train the SVR model for probability estimates, 0 or 1 (default 1)"

• cvtimelimit: Set a time limit (seconds) on individual cross-validation sub-calculation when searching over supplied SVM parameter ranges for optimal parameters. Only relevant if parameter ranges are used for SVM parameters such as cost, epsilon, gamma or nu. Default is 2 (seconds);
• splits: Number of subsets to divide data into when applying n-fold cross validation. Default is 5.
• gamma: Value(s) to use for LIBSVM kernel gamma parameter. Default is 15 values from 10^-6 to 10, spaced uniformly in log.
• cost: Value(s) to use for LIBSVM 'c' parameter. Default is 11 values from 10^-3 to 100, spaced uniformly in log.
• nu: Value(s) to use for LIBSVM 'n' parameter (nu of nu-SVC, and nu-SVR). Default is the set of values [0.2, 0.5, 0.8].
• outliernu: Value to use for nu in LIBSVM's one-class svm outlier detection. (0.05).

Algorithm

Svmda uses the LIBSVM implementation using the user-specified values for the LIBSVM parameters (see options above). See [1] for further details of these options.

The default SVMDA parameters cost, nu and gamma have value ranges rather than single values. This svm function uses a search over the grid of appropriate parameters using cross-validation to select the optimal SVM parameter values and builds an SVM model using those values. This is the recommended usage. The user can avoid this grid-search by passing in single values for these parameters, however.

See Also

analysis, svm