Research Article
Incremental Optimization Mechanism for Constructing a Decision Tree in Data Stream Mining
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Pseudocode of input and the test-then-train approach.
INPUT: | : A stream of sample | : A set of symbolic attributes | : Heuristic function using for node-splitting estimation | : One minus the desired probability of choosing a correct attribute at any given node | : The minimum number of samples between check node-splitting estimation | : A functional tree leaf strategy | OUTPUT: | HT: A decision tree | PROCEDURE: OVFDT (, , , , , ) | A data stream arrives | IF HT is null, THEN initializeHT(, , , , , ) | ELSE traverseHT(, , ) and update | Label as the predicted class among the samples seen so far | Let be the number of samples seen at the leaf | IF the samples seen so far at leaf do not all belong to the sameclass | and ( mod ) is zero, THEN doNodeSplitting(, , , , , ) | Return HT |
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