Research Article
Self-Trained LMT for Semisupervised Learning
Algorithm 1
The self-trained LMT algorithm.
Input: | LMT – Linear model trees, as base classifier | – Initial training dataset | – Ratio of labeled instances along | – initial labeled instances, | – initial unlabeled instances, | – Instances with Most Confident Predictions | MaxIter – number of maximum iterations performed | (1) Initialization: | Train LMT as base model on | (2) Loop for a number of iterations (MaxIter is equal to 40 for our implementation) | (a) Use LMT classifier to select the instances with Most Confident Predictions per iteration () | (b) Remove from and add them to | (c) In each iteration a few instances per class are removed from and added to | (d) Re-train LMT as base model on new enlarged | Output: | Use LMT trained on to predict class labels of the test cases. |
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