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Author: search.filters.author.Jearanaitanakij, KietikulSubject: search.filters.subject.Pruning

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    Spartan simplicity: A pruning algorithm for neural nets
    (2008-08-01)
    Jearanaitanakij, Kietikul
    ;
    Pinngern, Ouen
    Having more hidden units than necessary can produce a neural network that has a poor generalization. This paper proposes a new algorithm for pruning unnecessary hidden units away from the single-hidden layer feedforward neural networks, resulting in a Spartan network. Our approach is simple and easy to implement, yet produces a very good result. The idea is to train the network until it begins to lose its generalization. Then the algorithm measures the sensitivity and automatically prunes away the most irrelevant unit. We define this sensitivity as the absolute difference between the desirable output and the output of the pruned network. Unlike other pruning methods, our algorithm is distinct in calculating the sensitivity from the validation set, instead of the training set, without increasing the asymptotic time complexity of the back-propagation algorithm. In addition, for a classification problem, we raise a point that the sensitivities of some well-known pruning algorithms may still underestimate the irrelevance of hidden unit even though the validation set is used in measuring the sensitivity. We resolve this problem by considering the number of misclassified patterns as the main concern. The Spartan simplicity algorithm is applied to three artificial and seven standard benchmarks. In most problems, the algorithm can produce a compact-sized network with high generalization ability in comparison with other pruning algorithms. © 2008 World Scientific Publishing Company.
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    Hidden unit reduction of artificial neural network on English capital letter recognition
    (2006-12-01)
    Jearanaitanakij, Kietikul
    ;
    Pinngern, Ouen
    We present an analysis on the minimum number of hidden units that is required to recognize English capital letters of the artificial neural network. The letter font that we use as a case study is the System font. In order to have the minimum number of hidden units, the number of input features has to be minimized. Firstly, we apply our heuristic for pruning unnecessary features from the data set. The small number of the remaining features leads the artificial neural network to have the small number of input units as well. The reason is a particular feature has a one-to-one mapping relationship onto the input unit. Next, the hidden units are pruned away from the network by using the hidden unit pruning heuristic. Both pruning heuristic is based on the notion of the information gain. They can efficiently prune away the unnecessary features and hidden units from the network. The experimental results show the minimum number of hidden units required to train the artificial neural network to recognize English capital letters in System font In addition, the accuracy rate of the classification produced by the artificial neural network is practically high. As a result, the final artificial neural network that we produce is fantastically compact and reliable. © 2006 IEEE.