Changes in version 2.6.0
o
Two-stage easy-hard classifier added.
Changes in version 2.2.0
o
getClasses is no longer a slot of PredictParams. Every
predictor function needs to return either a factor vector of
classes, a numeric vector of class scores for the second class,
or a data frame with a column for the predicted classes and
another for the second-class scores.
o
Cross-validations which use folds ensure that samples belonging
to each class are in approximately the same proportions as they
are for the entire data set.
o
Classification can reuse fitted model from previous
classification by using previousTrained function.
o
Feature selection using gene sets and networks. Classification
can use meta-features derived from the individual features used
for feature selection.
o
tTestSelection function for feature selection based on ordinary
t-test statistic ranking. Now the default feature selection
function, if none is specified.
o
Tuning parameter optimisation metric is specified by providing
a tuneOptimise parameter to TrainParams rather than depending
on ResubstituteParams being used during feature selection.
Changes in version 2.0.0
o
Broad support for DataFrame and MultiAssayExperiment data sets
by feature selection and classification functions.
o
The majority of processing is now done in the DataFrame method
for functions that implement methods for multiple kinds of
inputs.
o
Elastic net GLM classifier and multinomial logistic regression
classifier wrapper functions.
o
Plotting functions have a new default style using a white
background with black axes.
o
Vignette simplified and uses a new mass cytometry data set with
clearer differences between classes to demonstrate
classification and its performance evaluation.
Changes in version 1.12.0
o
Alterations to make plots compatible with ggplot versions 2.2
and greater.
o
calcPerformance can calculate some performance metrics for
classification tasks based on data sets with more than two
classes.
o
Sample-wise metrics, like sample-specific error rate and
sample-specific accuracy are calculated by calcPerformance and
added to the ClassifyResult object, rather than by
samplesMetricMap and being inaccessible to the end-user.
Changes in version 1.10.0
o
errorMap replaced by samplesMetricMap. The plot can now show
either error rate or accuracy.
Changes in version 1.8.0
o
Ordinary k-fold cross-validation option added.
o
Absolute difference of group medians feature selection function
added.
Changes in version 1.4.0
o
Weighted voting mode that uses the distance from an observation
to the nearest crossover point of the class densities added.
o
Bartlett Test selection function included.
o
New class SelectResult. rankPlot and selectionPlot can
additionally work with lists of SelectResult objects. All
feature selection functions now return a SelectResult object or
a list of them.
o
priorSelection is a new selection function for using features
selected in a prior cross validation for a new data set
classification.
o
New weighted voting mode, where the weight is the distance of
the x value from the nearest crossover point of the two
densities. Useful for predictions with skewed features.
Changes in version 1.2.0
o
More classification flexibility, now with parameter tuning
integrated into the process.
o
New performance evaluation functions, such as a ROC curve and a
performance plot.
o
Some existing predictor functions are able to return class
scores, not just class labels.
Changes in version 1.0.0
o
First release of the package, which allows parallelised and
customised classification, with many convenient performance
evaluation functions.