Combining Generalizers Using Partitions Of The Learning Set

Figure 1. A stylized depiction of how to combine the two generalizers G 1 and G 2 via stacked generalization. A learning set L is symbolically depicted by the full ellipse. We want to guess what output corresponds to the question q. To do this we create a CVPS of L; one of these partitions is shown, splitting L into {(x, y)} and {L-(x, y)}. By training both G 1 and G 2 on {L-(x, y)}, asking both of them the question x, and then comparing their guesses to the correct guess y, we construct a single input-output pair (indicated by one of the small solid ellipses) of a new learning set L'. This input-output pair gives us information about how to go from guesses made by the two generalizers to a correct output. The remaining partitions of L give us more of such information; they give us the remaining elements of L'. We now train a generalizer on L' and ask it the two-dimensional question {G 1 (L; q), G 2 (L; q)}. The answer is our final guess for what output corresponds to q.