Arcing methods are a broad category of Adaptive Resampling and Combining approaches for boosting machine learning and statistical techniques' performance.
ADABOOST and bagging are two prominent examples.
In general, these strategies apply a learning technique to a
training set repeatedly, such as a decision tree, and then reweight, or
resample, the data and refit the learning technique to the data.
This results in a set of learning rules.
New observations are passed through all members of the
collection, and the predictions or classifications are aggregated by averaging
or a majority rule prediction to generate a combined result.
These strategies may provide findings that are significantly
more accurate than a single classifier, but being less interpretable than a
single classifier.
They can build minimum (Bayes) risk classifiers, according
to research.
See Also:
ADABOOST, Bootstrap AGGregation
