ECE/CS/ME 539 Artificial Neural Networks Final Project

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 12-19-03

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

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)

Results Both Neural Net and Learning Tree had almost identical test set classification rates Learning Tree = 95.789 % BP Neural Net = 95.105 % Learning Tree runs much faster, always consistent Neural Net only consistent when train set not permutated

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