Covering algorithm


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covering algorithm

A covering algorithm, in the context of propositional learning systems, is an algorithm that develops a cover for the set of positive examples - that is, a set of conjunctive expressions that account for all the examples but none of the non-examples.

The algorithm - given a set of examples:

Start with an empty cover.

Select an example.

Find the set of all conjunctive expressions that cover that example.

Select the "best" expression x from that set, according to some criterion (usually "best" is a compromise between generality and compactness and readability).

Add x to the cover.

Go to step 2, unless there are no examples that are not already covered (in which case, stop).


Source : INWS machine learning dictionary ]