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This dataset has been developed to help evaluate a "hybrid" learning algorithm ("KBANN") that uses examples to inductively refine preexisting knowledge. Using a "leave-one-out" methodology, the following errors were produced by various ML algorithms. (See Towell, Shavlik, & Noordewier, 1990, for details.) System -- Errors -- Comments ---------------------------------------------------------------- KBANN -- 4/106 -- a hybrid ML system BP -- 8/106 -- std backprop with one hidden layer O'Neill -- 12/106 -- ad hoc technique from the bio. lit. Near-Neigh -- 13/106 -- a nearest-neighbor algo (k=3) ID3 -- 19/106 -- Quinlan's decision-tree builder Type of domain: non-numeric, nominal (one of A, G, T, C) Note: DNA nucleotides can be grouped into a hierarchy, as shown below: X (any) / \ (purine) R Y (pyrimidine) / \ / \ A G T C Here is that hierachy in a text-friendly format: X (any) . R (purine) . . A . . G . Y (pyrimidine) . . T . . C

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