pRAM n-tuple Classifier - a new architecture of probabilistic RAM neurons for classification problems

Adeodato, Paulo J. L.; Neto, Rosalvo Ferreira de Oliveira

doi:10.1109/ijcnn.2010.5596779

Cited by 3 publications

(1 citation statement)

References 17 publications

(20 reference statements)

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“…Most of them focus on addition of noise during neural networks training as a tool to improve the generalization capability and convergence time [11] [12] [13] [14]. However, there are limited studies comparing the effects of adding artificial noise to traditional approaches as a way to address particular data constraints such as small and/or unbalanced data sets and considering others relevant aspects such as correlation between variables.…”

Section: Training With Noisementioning

confidence: 99%

PCA and Gaussian noise in MLP neural network training improve generalization in problems with small and unbalanced data sets

Silva

Adeodato

2011

The 2011 International Joint Conference on Neural Networks

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Machine learning approaches have been successfully applied for automatic decision support in several domains. The quality of these systems, however, degrades severely in classification problems with small and unbalanced data sets for knowledge acquisition. Inherent to several realworld problems, data sets with these characteristics are the reality to be tackled by learning algorithms, but the small amount of data affects the classifiers' generalization power while the imbalance in class distribution makes the classifiers biased towards the larger classes. Previous work had addressed these data constraints with the addition of Gaussian noise to the input patterns' variables during the iterative training process of a MultiLayer perceptron (MLP) neural network (NN). This paper improves the quality of such classifier by decorrelating the input variables via a Principal Component Analysis (PCA) transformation of the original input space before applying additive Gaussian noise to each transformed variable for each input pattern. PCA transformation prevents the conflicting effect of adding decorrelated noise to correlated variables, an effect which increases with the noise level. Three public data sets from a well-known benchmark (Proben1) were used to validate the proposed approach. Experimental results indicate that the proposed methodology improves the performance of the previous approach being statistically better than the traditional training method (95% confidence) in further experimental set-ups.

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Section: Training With Noisementioning

confidence: 99%

PCA and Gaussian noise in MLP neural network training improve generalization in problems with small and unbalanced data sets

Silva

Adeodato

2011

The 2011 International Joint Conference on Neural Networks

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PAN RAM Bootstrapping Regressor - A New RAM-Based Architecture for Regression Problems

Souza

Lima²,

Lins

et al. 2022

Lecture Notes in Computer Science

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Polynomial approximation RAM neuron capable of handling true continuous input variables

Adeodato

Neto

2016

2016 International Joint Conference on Neural Networks (IJCNN)

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pRAM n-tuple Classifier - a new architecture of probabilistic RAM neurons for classification problems

Abstract: RAM-based neural networks have been in the market long before the MultiLayer Perceptrons but their developments have been concentrated in a few periods and research groups. Recently, one of the winners of the PAKDD

Cited by 3 publications

References 17 publications

PCA and Gaussian noise in MLP neural network training improve generalization in problems with small and unbalanced data sets

PCA and Gaussian noise in MLP neural network training improve generalization in problems with small and unbalanced data sets

PAN RAM Bootstrapping Regressor - A New RAM-Based Architecture for Regression Problems

Polynomial approximation RAM neuron capable of handling true continuous input variables

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