alzami1986/ensemblelearningexercises

Exercises done during the discipline of Ensemble Learning (PhD graduation)

Ensemble Learning Exercises

Exercises done during the discipline of Ensemble Learning (Machine Learnig and Signal Processing PhD)

Exercise01

In this exercise, I implemented and evaluated the Bagging ensemble method. Two data sets from UCI were used for evaluation. Three kinds of classifiers were used: Decision Trees, k-Nearest Neighbors (kNN), and Multilayer Perceptrons (kNN). More details of the experiments are described in the report.

Exercise03

I implemented an Ensemble Pruning method via Individual Contribution ordering (EPIC). I compared the performance of this method with the performance of Bagging, using a dataset from UCI. More details of the experiments are described in the report.

Exercise04

In this exercise, I implemented two methods of Dynamic Classifier Selection from a pool of classifiers: selection via Overall Local Accuracy (OLA) and via Local Class Accuracy (LCA). the performance of these methods with the performance of Bagging, using a dataset from UCI. More details of the experiments are described in the report.

Conclusion Project

The project is to replicate the results reported in the following paper:

[Guo, L. and Boukir, S.] Margin-based ordered aggregation for ensemble pruning, Pattern Recognition Letters, v. 34, pp. 603-609, 2013.

More details are described in the report.