1. ECE/CS/ME 539 Artificial Neural Networks Final Project

2. A Comparison of a Learning Decision Tree and a 2-Layer Back-Propagation Neural Network on classifying a car purchase using a 2-Layer Back-Propagation Neural Network constructed in Java
Steve Ludwig

3. Introduction/Motivation
Studied Decision Learning Trees
Same purpose as pattern classifying BP Neural Nets
Wanted to compare/contrast using identical data
Built own 2-layer back-propagation neural network in Java with customizable attributes

4. Data Learning Tree uses text-based attributes/values
Constructs ‘tree’ with nodes as attributes
Leaf nodes classify as positive or negative
Had to convert to numeric values for BP Neural Net
e.g. acceptable case = 1, unacceptable case = 0
Could customize Neural Net parameters
Tried different learning rates, epochs, permutation of train set (to avoid overfitting)

5. Results
Both Neural Net and Learning Tree had almost identical test set classification rates
Learning Tree = %
BP Neural Net = %
Learning Tree runs much faster, always consistent
Neural Net only consistent when train set not permutated

6. Conclusions
Learning Tree works faster, great accuracy, can use text-based attributes
BP Neural Net has more flexibility, can be modified to work better (more hidden layers), still good classification rate