Title :
Using graphs to analyze high-dimensional classifiers
Author :
Melnik, Ofer ; Pollack, Jordan
Author_Institution :
Volen Center for Complex Syst., Brandeis Univ., Waltham, MA, USA
Abstract :
We present a method to extract qualitative information from any classification model that uses decision regions, independent on the dimensionality of the data and model. The qualitative information can be directly used to analyze the classification strategies employed by a model, and also to directly compare strategies across different models. We apply the method to compare between two types of classifiers using real-world high-dimensional data
Keywords :
decision trees; feature extraction; graph theory; neural nets; pattern classification; decision regions; generalisation; graph theory; high-dimensional data classifiers; neural nets; qualitative information extraction; Classification tree analysis; Data mining; Feedforward neural networks; Feedforward systems; Graphical models; Information analysis; Manifolds; Neural networks; Performance analysis; Support vector machines;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.861345
