Self-Interacting Proteins Prediction from PSSM Based on Evolutionary Information

Algorithm 1

The pseudocodes of rotation forest algorithm.

 Training Phase

 Input

  X: the training samples.

  Y: the labels of training samples.

  L: the ensemble size of classifiers.

  K: the number of subsets.

  R: the proportion of resampling new samples from original samples (R = 0.75).

  for i = 1, ..., L

   construct sparse rotation matrix .

   divided the total samples into K disjoint subsets randomly.

  for j = 1, ..., K

   form a new matrix

   using bootstrap algorithm to obtain R proportion subset .

   using PCA on to obtain coefficients in a matrix .

   build decision tree .

 Classification Phase

 Input

  x: the test samples.

  for n = 1, ..., L

   calculate the probability of each classes.

  Finally, using the largest average confidence to classification.