| D. B. Skillicorn. A parallel tree di#erence algorithm. Information Processing Letters, 60(5):231--235, 1996. |
....to be similar to the old one, i.e. using either the same splitting attributes or the same splittings in the decision tree construction. This is a severe restriction because important changes may vanish from classifiers even though they exist in the data. The work on finding tree di#erences [16] is not applicable here because dissimilar decision trees could produce similar classification. Also, changes of classification depend not only on the structure of rules, but also on the statistical property of rules. 3 The Proposed Approach We consider classifiers given by a set of rules. A ....
D. B. Skillicorn. A parallel tree di#erence algorithm. Information Processing Letters, 60(5):231--235, 1996.
....to be similar to the old one, i.e. using either the same splitting attributes or the same splittings in the decision tree construction. This is a severe restriction because important changes may vanish from classifiers even though they exist in the data. The work on finding tree di#erences [16] is not applicable here because dissimilar decision trees could produce similar classification. Also, changes of classification depend not only on the structure of rules, but also on the statistical property of rules. 3 The Proposed Approach We consider classifiers given by a set of rules. A ....
D. B. Skillicorn. A parallel tree di#erence algorithm. Information Processing Letters, 60(5):231--235, 1996.
No context found.
D. B. Skillicorn. A parallel tree di#erence algorithm. Information Processing Letters, 60(5):231--235, 1996.
No context found.
D. B. Skillicorn. A parallel tree di#erence algorithm. Information Processing Letters, 60(5):231-- 235, 1996.
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