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.