[Paper Review] Pattern Classification using Simplified Neural Networks

S. M. Kamruzzaman, Ahmed Ryadh Hasan|arXiv (Cornell University)|Sep 25, 2010

Neural Networks and Applications14 references3 citations

TL;DR

This paper proposes a simplified neural network approach for pattern classification by applying a pruning algorithm to eliminate redundant weights, inputs, and hidden units, significantly reducing network complexity while maintaining strong generalization performance. Experimental results on benchmark datasets demonstrate that the pruned networks achieve high accuracy with minimal parameters, effectively opening the 'black box' of traditional ANNs.

ABSTRACT

In recent years, many neural network models have been proposed for pattern classification, function approximation and regression problems. This paper presents an approach for classifying patterns from simplified NNs. Although the predictive accuracy of ANNs is often higher than that of other methods or human experts, it is often said that ANNs are practically "black boxes", due to the complexity of the networks. In this paper, we have an attempted to open up these black boxes by reducing the complexity of the network. The factor makes this possible is the pruning algorithm. By eliminating redundant weights, redundant input and hidden units are identified and removed from the network. Using the pruning algorithm, we have been able to prune networks such that only a few input units, hidden units and connections left yield a simplified network. Experimental results on several benchmarks problems in neural networks show the effectiveness of the proposed approach with good generalization ability.

Motivation & Objective

• To address the 'black box' problem of traditional artificial neural networks (ANNs) by reducing their complexity.
• To improve interpretability and efficiency of ANNs through systematic pruning of redundant components.
• To maintain high predictive accuracy while minimizing the number of input units, hidden units, and connections.
• To evaluate the effectiveness of the simplified network on standard benchmark classification problems.
• To demonstrate that reduced complexity does not compromise generalization ability.

Proposed method

• A pruning algorithm is applied to identify and remove redundant weights in trained neural networks.
• Redundant input and hidden units are detected and eliminated based on their contribution to network output.
• The pruning process iteratively removes low-impact connections and neurons, simplifying the architecture.
• The simplified network is retrained to maintain performance after structural reduction.
• The method is applied to multiple benchmark pattern classification datasets to evaluate generalization.
• Network complexity is minimized while preserving classification accuracy through iterative pruning and retraining.

Experimental results

Research questions

• RQ1 Can neural network complexity be significantly reduced without degrading classification accuracy?
• RQ2 To what extent can redundant weights, inputs, and hidden units be removed while maintaining generalization?
• RQ3 Does pruning improve the interpretability of ANNs without sacrificing predictive performance?
• RQ4 How effective is the proposed pruning algorithm across diverse benchmark pattern classification tasks?
• RQ5 Can simplified networks achieve comparable or better performance with fewer parameters?

Key findings

• The pruning algorithm successfully reduced the number of input units, hidden units, and connections in the network.
• The simplified networks maintained strong generalization performance on benchmark classification problems.
• Significant reduction in network complexity was achieved with minimal loss in predictive accuracy.
• The approach effectively mitigates the 'black box' nature of ANNs by simplifying their architecture.
• The method demonstrated robustness across multiple benchmark datasets, confirming its effectiveness.
• The final simplified networks achieved high classification accuracy with a minimal number of parameters.