Research Article

Incremental Optimization Mechanism for Constructing a Decision Tree in Data Stream Mining

Pseudocode 1

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