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The classifiers discussed in the previous chapters expect all attribute values to be presented at the same time. Such a scenario, however, has its flaws. Thus a physician seeking to come to grips with the nature of her patient’s condition often has nothing to begin with save a few subjective symptoms. And so, to narrow the field of diagnoses, she prescribes lab tests, and, based on the results, perhaps other tests still. At any given moment, then, the doctor considers only “attributes” that promise to add meaningfully to her current information or understanding. It would be absurd to ask for all possible lab tests (thousands and thousands of them) right from the start.
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Breiman, L., Friedman, J., Olshen, R., & Stone, C. J. (1984). Classification and regression trees. Belmont: Wadsworth International Group. MATH
Hunt, E. B., Marin, J., & Stone, P. J. (1966). Experiments in induction. New York: Academic Press.
Quinlan, J. R. (1979). Discovering rules by induction from large collections of examples. In D. Michie (Ed.) Expert systems in the micro electronic age. Edinburgh: Edinburgh University Press.
Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1, 81–106.
Quinlan, J. R. (1993). C4.5: Programms for machine learning. San Mateo: Morgan Kaufmann.
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