This toolbox contains programs for four different multi-instance learners and their ensemble versions. In detail, this toolbox contains three parts:
1)Data Preparation
  This part includes three components:
    1.1) Original Musk data [1] from UCI machine learning repository
    1.2) Preprocessed Musk data for further usage
    1.3) Functions for dividing the Musk data into 10 folds, which are called before conducting 10-fold cross-validation experiments
    1.4) Function for performing min max normalization

2)Individual Algorithm
  This part includes programs for four different multi-instance learners:
    2.1) Iterated-Discrim APR [1], the main function is 'IDAPR'
    2.2) Citation-kNN [2], the main function is 'CKNN'
    2.3) Diverse Density [3], the main function is 'maxDD'
    2.4) EM-DD [4], the main function is 'EMDD'
  For more details of the above algorithms, please refer to the correponding codes and comments.

3)Ensemble Algorithm
  This part includes programs for the Ensemble of four different multi-instance learners:
    3.1) Ensemble of Iterated-Discrim APR
    3.2) Ensemble of Citation-kNN
    3.3) Ensemble of Diverse Density
    3.4) Ensemble of EM-DD
  For more details of the functionality of the above algorithms, please refer to [5] and the corresponding codes and comments.

[1] T.G. Dietterich, R.H. Lathrop, and T. Lozano-Perez. Solving the multiple-instance problem with axis-parallel rectangles. Artificial Intelligence, 89(1-2): 31-71, 1997.
[2] J. Wang and J.-D. Zucker. Solving the multiple-instance problem: a lazy learning approach. In: Proceedings of the 17th International Conference on Machine Learning, San Francisco, CA: Morgan Kaufmann, 1119-1125, 2000.
[3] Maron O. Learning from ambiguity [PhD dissertation]. Department of Electrical Engineering and Computer Science, MIT, 1998
[4] Q. Zhang and S. A. Goldman. EM-DD: an improved multi-instance learning technique. In: Advances in Neural Processing Systems 14, Cambridge, MA:MIT Press, 1073-1080, 2001.
[5] Z.-H. Zhou and M.-L. Zhang. Ensembles of multi-instance learners. In: Lecture Notes in Computer Science 2837, Berlin: Springer-Verlag, 2003, 492-502.

