A comparison of Adaboosted FLD-based classifier with several standard classification algorithms

by Zehra Hasanain (2003-03-0075)

Time, Date & Venue

10:45 A.M. Saturday, 26th November 2005
Room 427 CS Dept.

Abstract:

The purpose of this survey is to compare the performance of a relatively new algorithm, AdaboostFLD, against the performance of several standard pattern classification algorithms, including neural networks, decision trees, support vector machines, as well as another Adaboost-based algorithm that uses decision stumps as the weak classifier. Seven binary two-dimensional datasets have been used in the experiments, out of which four are separable and three are non-separable. Training error, testing error and training times have been compared for the above-mentioned algorithms. The results obtained seem to show a favorable trend towards AdaboostFLD. This survey describes the experiments that have been conducted, and discusses in detail the results obtained in the course of this study.