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Domain density description for multiclass pattern classification with reduced computational load

pattern RECOGNITION 2008年第6期41卷 1997-2009页

作者： Kang, Woo-Sung Choi, JinYung Seoul Natl Univ Sch Elect & Comp Engn Kwanak Ku Seoul 151744 South Korea Seoul Natl Univ Seoul 151600 South Korea

We propose a novel classification method that can reduce the computational cost of training and testing for multiclass problems. The proposed method uses the distance in feature space between a test sample and high-density region or domain that can be described by support vector learning. The proposed method shows faster training speed and has ability to represent the nonlinearity of data structure using a smaller percentage of available data sample than the existing methods for multiclass problems. To demonstrate the potential usefulness of the proposed approach, we evaluate the performance about artificial and actual data. Experimental results show that the proposed method has better accuracy and efficiency than the existing methods. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： multiclass pattern classification computational load reduction support vector learning

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Optimized Neural Network for Improved Electrocardiogram Beat classification 6

Optimized Neural Network for Improved Electrocardiogram Beat...

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IEEE 6th International Advance Computing Conference (IACC)

作者： Dash, Sanjit K. Rao, G. Sasibhushana Raajdhani Engn Coll Dept ECE Bhubaneswar Orissa India Andhra Univ Coll Engn Dept ECE Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9781467382861

The performance of neural network as a classifier depends on several factors such as initialization of weights, its architecture, between class imbalance in the dataset, activation function etc. Though a three layered neural network is able to approximate any non-linear function, number of neurons in the hidden layer plays a significant roll in the performance of the classifier. In this study, the importance of the number of hidden layer neurons of neural network is analyzed for the classification of ECG signals. Five different arrhythmias and the normal beat are classified for different number of hidden layer neurons to examine the performance of the classifier. In this study we get the best number as 35. After the training of the neural network with the optimized number of neurons in the hidden layer, we have tested the performance with three different datasets. The average sensitivity, specificity and accuracy achieved is 94.91%, 99.69% and 99.46% respectively.

关键词： Optimized Artificial Neural Network ECG Arrhythmia multiclass pattern classification Backpropagation Algorithm

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On the Mean Accuracy of Hierarchical Classifiers

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IEEE Transactions on Computers 1978年第8期C-27卷 771-776页

作者： Kulkarni, Ashok V. Coulter Biomedical Research Laboratory Waltham MA 02154 United States

A performance measure is derived for a multiclass hierarchical classifier under the assumption that a maximum likelihood rule is used at each node and the features at different nodes of the tree are class-conditionally statistically independent. The mean accuracy of an estimated hierarchical classifier is then defined as its performance averaged across all classification problems, when an estimated decision rule is used at every node. For a balanced binary decision tree, it is shown that there exists an optimum number of quantization levels for the features which maximizes the mean accuracy. The optimum quantization level increases with the number of training samples per class available to estimate the node decisions and is a nondecreasing function of the depth of the tree. Copyright © 1978 by The Institute of Electrical and Electronics Engineers. Inc.

关键词： Hierarchical classifiers independent measurement multiclass pattern classification quantization complexity sample size

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