Research Article
A Semisupervised Cascade Classification Algorithm
Algorithm 2
The self-trained (NB
C4.5) algorithm.
Algorithm Self-trained (NB C4.5) | Input: | NBC4.5 – Cascading NB and C4.5, as base classifier | – Initial training dataset | – initial labeled examples, | – initial unlabeled examples, | – Examples with Most Confident Predictions | AccT – Threshold of acceptance | MaxIter – number of maximum iterations performed | Initialization: | Train NBC4.5 as base model on | Loop for a number of iterations (MaxIter is equal to 40 for our implementation) | Use NBC4.5 classifier to select the examples with Most Confident Predictions per iteration () | Remove from and add them to | In each iteration a few examples per class are removed from and added to | Re-train NBC4.5 as base model on new enlarged | Output: | Use NBC4.5 trained on to predict class labels of the test cases. |
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