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IEEE Trans. Neural Netw. Learning Syst. 01/2012; 23:480-491.
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ABSTRACT: Text categorization usually suffers from a huge-scale number of features. Most of those are irrelevant and noise which could mislead the classifier. In order to improve the efficiency and effectiveness for text categorization, feature selection is often performed. In this paper, a novel feature selection approach for dealing with text categorization, called Maximum Information Metric (MIM), is proposed to get good quality terms of documents. This method exploits the weight of term and document frequency to construct the correlation between a term and each class. It aims to maximize the differences of term over each class based on information theory. We design a better evaluation function to yield a kind of ranking of the features. Experimental results on the standard Reuters-21578 and 20-Newsgroups corpus show that the new feature selection approach outperforms the classic methods including Information Gain (IG), Chi-square statistic (CHI) in a context of text categorization.
Information Science and Engineering (ICISE), 2009 1st International Conference on; 01/2010
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ABSTRACT: How to design proper architectures of neural networks for solving given problems is an important issue in neural network research. Nowadays, the existing training algorithms of neural networks only focus on adjusting neural networks' weights to improve training accuracy, and few of them adaptively adjust the networks' architecture. However, the architecture is indeed very critical for training neural networks to have high performance and needs to be coped with in the training process. In this paper, we present a new training algorithm of Madalines, which takes not only weight but also architecture adjusting into consideration. The algorithm can thus train Madalines with smaller architecture and higher generalization ability. Experimental results have demonstrated that our algorithm is effective.
Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on; 11/2009
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Inf. Process. Manage. 01/2009; 45:413-426.
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Neural Computing and Applications. 01/2009; 18:957-965.
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ABSTRACT: Fuzzy clustering is a useful tool for identifying relevant subsets of microarray data. This paper proposes a fuzzy clustering method for microarray data analysis. An advantage of the method is that it used a combination of the fuzzy c-means and the principal component analysis to identify the groups of genes that show similar expression patterns. It allows a gene to belong to more than a gene expression pattern with different membership grades. The method is suitable for the analysis of large amounts of noisy microarray data.
Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine 11/2008; 222(7):1143-8. · 1.21 Impact Factor
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Progress in WWW Research and Development, 10th Asia-Pacific Web Conference, APWeb 2008, Shenyang, China, April 26-28, 2008. Proceedings; 01/2008
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ABSTRACT: Multiple classifier systems (MCSs) have been shown theoretically and empirically to outperform a single classifier in many applications. However, many ensemble training algorithms sometimes create a very large MCS which is combined by many individual classifiers. A large MCS not only consumes computational resources but also decreases the effectiveness. One of the solutions is the pruning method. It reduces the number of individual classifiers inside an MCS that maintains the performance well or is just slightly worse than the original one. In this paper, a new pruning method, called NNEPSM, for Neural Network ensemble based on a sensitivity measure is proposed. The classifiers which have less impact to the final output of MCS will be removed. The advantages of this method include efficient performance, low-complexity and independence on training method. NNEPSM has been applied in Web applications and other benchmark dataset. The experimental results showed that our approach performs well using different datasets.
Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on; 11/2007
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Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Montréal, Canada, 7-10 October 2007; 01/2007
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ABSTRACT: The sensitivity of a neural network's output to its input and weight perturbations is an important measure for evaluating the network's performance. In this letter, we propose an approach to quantify the sensitivity of Madalines. The sensitivity is defined as the probability of output deviation due to input and weight perturbations with respect to overall input patterns. Based on the structural characteristics of Madalines, a bottom-up strategy is followed, along which the sensitivity of single neurons, that is, Adalines, is considered first and then the sensitivity of the entire Madaline network. By means of probability theory, an analytical formula is derived for the calculation of Adalines' sensitivity, and an algorithm is designed for the computation of Madalines' sensitivity. Computer simulations are run to verify the effectiveness of the formula and algorithm. The simulation results are in good agreement with the theoretical results.
Neural Computation 12/2006; 18(11):2854-77. · 1.88 Impact Factor
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ABSTRACT: In this paper, the sensitivity of Adalines to weight perturbation is discussed. According to the discrete feature of Adalines' input and output, the sensitivity is defined as the probability of an Adaline's erroneous outputs due to weight perturbation with respect to all possible inputs. By means of hypercube model and analytical geometry method, a heuristic algorithm is given to accurately compute the sensitivity. The accuracy of the algorithm is verified by computer simulations.
IEEE Transactions on Neural Networks 04/2006; 17(2):515-9. · 2.95 Impact Factor
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Proceedings of the International Joint Conference on Neural Networks, IJCNN 2006, part of the IEEE World Congress on Computational Intelligence, WCCI 2006, Vancouver, BC, Canada, 16-21 July 2006; 01/2006
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ACM Trans. Comput.-Hum. Interact. 01/2006; 13:268-307.
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ABSTRACT: Enhancing the reserved graph grammar (RGG) formalism, this paper introduces a size-increasing condition on the structure of graph grammars' productions to simplify the definition of graph grammars, and a general parsing algorithm to extend the power of the RGG parsing algorithm.
Visual Languages and Human-Centric Computing, 2005 IEEE Symposium on; 10/2005
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Advances in Machine Learning and Cybernetics, 4th International Conference, ICMLC 2005, Guangzhou, China, August 18-21, 2005, Revised Selected Papers; 01/2005
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ABSTRACT: This paper presents an approach to determine the relevance of individual input attributes for trained Multilayer Perceptrons (MLPs). To reflect the impact of an input attribute on the output of an MLP, the relevance is aimed at representing the output sensitivity of the MLP to the attribute variation. The sensitivity is defined as the mathematical expectation of output deviations of an MLP due to its input deviation with respect to overall input patterns. The basic idea for the introduction of such a relevance measure is that a well-trained MLP can capture salient features of the problem it deals with and thus become more sensitive to those input attributes that make more contributions to the MLP's behavior. The relevance can be employed as a relative criterion for assessing individual input attributes. The results from the experiments on two typical problems demonstrate the effectiveness of the relevance in identifying irrelevant input attribute.
Systems, Man and Cybernetics, 2003. IEEE International Conference on; 11/2003
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ABSTRACT: The sensitivity of a neural network's output to its input perturbation is an important issue with both theoretical and practical values. In this article, we propose an approach to quantify the sensitivity of the most popular and general feedforward network: multilayer perceptron (MLP). The sensitivity measure is defined as the mathematical expectation of output deviation due to expected input deviation with respect to overall input patterns in a continuous interval. Based on the structural characteristics of the MLP, a bottom-up approach is adopted. A single neuron is considered first, and algorithms with approximately derived analytical expressions that are functions of expected input deviation are given for the computation of its sensitivity. Then another algorithm is given to compute the sensitivity of the entire MLP network. Computer simulations are used to verify the derived theoretical formulas. The agreement between theoretical and experimental results is quite good. The sensitivity measure can be used to evaluate the MLP's performance.
Neural Computation 02/2003; 15(1):183-212. · 1.88 Impact Factor
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ABSTRACT: An important issue in the design and implementation of a neural
network is the sensitivity of its output to input and weight
perturbations. In this paper, we discuss the sensitivity of the most
popular and general feedforward neural networks-multilayer perceptron
(MLP). The sensitivity is defined as the mathematical expectation of the
output errors of the MLP due to input and weight perturbations with
respect to all input and weight values in a given continuous interval.
The sensitivity for a single neuron is discussed first and an analytical
expression that is a function of the absolute values of input and weight
perturbations is approximately derived. Then an algorithm is given to
compute the sensitivity for the entire MLP. As intuitively expected, the
sensitivity increases with input and weight perturbations, but the
increase has an upper bound that is determined by the structural
configuration of the MLP, namely the number of neurons per layer and the
number of layers. There exists an optimal value for the number of
neurons in a layer, which yields the highest sensitivity value. The
effect caused by the number of layers is quite unexpected. The
sensitivity of a neural network may decrease at first and then almost
keeps constant while the number increases
IEEE Transactions on Neural Networks 12/2001; · 2.95 Impact Factor
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Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, IJCAI 2001, Seattle, Washington, USA, August 4-10, 2001; 01/2001
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ABSTRACT: An important issue in the design and implementation of neural
networks is the sensitivity of neural network output to parameter
perturbations. Past research in this area has focused on network
sensitivity analysis after training. Very few research projects have
considered sensitivity analysis as a design issue prior to network
implementation. The authors discuss the sensitivity of the most popular
and general feedforward networks (multilayer perceptron (MLP)) to its
input perturbation. The sensitivity is defined as the mathematical
expectation of output errors of the MLP arising from input error with
respect to all input and weight values in a given continuous interval.
The sensitivity for a single neuron is discussed first, and an
analytical expression that is a function of the input error is
approximately derived. Then an algorithm is given to compute the
sensitivity for an entire MLP network. The theoretical results of the
derived formula were shown to agree with experimental results. By
analyzing the derived analytical expression and implementing the given
algorithm on a number of representative MLP networks, some significant
observations on the behavior of sensitivity are discovered, which could
be useful for network design consideration
Systems, Man, and Cybernetics, 2000 IEEE International Conference on; 02/2000
