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ECE/CS/ME 539 Artificial Neural Networks Final Project PowerPoint Presentation
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ECE/CS/ME 539 Artificial Neural Networks Final Project

ECE/CS/ME 539 Artificial Neural Networks Final Project

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ECE/CS/ME 539 Artificial Neural Networks Final Project

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  1. ECE/CS/ME 539Artificial Neural NetworksFinal 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 12-19-03

  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 = 95.789 % • BP Neural Net = 95.105 % • 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