Nearest Subclass Classifier

Python implementation of the Nearest Subclass Classifier (NSC), based on Maximum Variance Clusters (MVC).

The classification algorithm is based on the work published in the papers:

• The nearest subclass classifier: a compromise between the nearest mean and nearest neighbor classifier (Veenman and Reinders, 2005).

• A maximum variance cluster algorithm (Veenman, Reinders, Backer, 2002).

Follows a brief description of how the scripts work, however probably nothing of this is going to help you much unless you are already familiar with the algorithm.

• do_mvc.py creates the prototypes from a text file. Requires as inputs:
  1. filename
  2. value of sigma-square-max
  3. column separator
  4. position of class identifier within the line
• do_nsc.py classifies points from a file, given a file with prototypes. Requires as inputs:
  1. file of non-classified points
  2. prototypes file
  3. column separator
• do_cross.py finds the optimal value of sigma-square-max. Requires as inputs:
  1. file with points
  2. number of iterations
  3. column separator
  4. position of class identifier within the line
• do_test.py tests NSC/MVC on a set of already-classified points. Requires as inputs:
  1. file with points
  2. value of sigma-square-max
  3. column separator
  4. position of class identifier within the line

More details (in Italian) here.