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Published byRodger Washington Modified over 6 years ago
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ECE/CS/ME 539 Artificial Neural Networks Final Project
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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
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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
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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)
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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
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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
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