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Journal ArticleDOI

A Classification Approach Based on Evolutionary Neural Networks

TL;DR: A classification approach based on evolutionary neural networks (CABEN) is presented, which establishes classifiers by a group of three-layer feed-forward neural networks, which has the better performance in classification precision compared with Bayesian and decision trees.
Abstract: * Supported by the Natural Science Foundation under grant NO. 10476006 Abstract: Classification is important in data mining and machine learning. In this paper, a classification approach based on evolutionary neural networks (CABEN) is presented, which establishes classifiers by a group of three-layer feed-forward neural networks. The neural networks are trained by an improving algorithm synthesizing modified Evolutionary Strategy and Levenberg-Marquardt optimization method. The class label of the identifying data can first be evaluated by each neural network, and the final classification result is obtained according to the absolute-majority-voting rule. Experimental results show that the algorithm is effective for the classification, and has the better performance in classification precision, comparing with Bayesian and decision trees, especially for the complex classification problems with many classes.
Citations
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01 Jan 2002

9,314 citations

Journal ArticleDOI
TL;DR: This paper describes the latest progress of ELM in recent years, including the model and specific applications of ELm, and finally points out the research and development prospects ofELM in the future.
Abstract: Extreme learning machine (ELM) is a new learning algorithm for the single hidden layer feedforward neural networks Compared with the conventional neural network learning algorithm it overcomes the slow training speed and over-fitting problems ELM is based on empirical risk minimization theory and its learning process needs only a single iteration The algorithm avoids multiple iterations and local minimization It has been used in various fields and applications because of better generalization ability, robustness, and controllability and fast learning rate In this paper, we make a review of ELM latest research progress about the algorithms, theory and applications It first analyzes the theory and the algorithm ideas of ELM, then tracking describes the latest progress of ELM in recent years, including the model and specific applications of ELM, finally points out the research and development prospects of ELM in the future

429 citations


Cites background or methods from "A Classification Approach Based on ..."

  • ...Extreme learning machine is used in the fields such as signal processing (Mao 2002), automatic control (Mao and Huang 2005), image processing (Huang et al. 2011), market analysis, aviation and aerospace and medical diagnosis (Shang et al. 2005), etc....

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  • ...2011), market analysis, aviation and aerospace and medical diagnosis (Shang et al. 2005), etc....

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Journal ArticleDOI
TL;DR: This paper proposes a Collaborative Extreme Learning Machine (CELM) with a Confidence Interval (CI), which is an enhanced version of the traditional Extreme learning machine (ELM), and improves the prediction accuracy by considering where plausible predictions lie.

14 citations

Proceedings ArticleDOI
Bin Wang1, Bin Yang1, Jinfang Sheng1, Mengsheng Chen, Guoqiang He 
23 Jan 2009
TL;DR: The proposed conjugate gradient algorithm has global convergence, optimizes the learning steps using new line search rules and improves the convergence speed, and the new algorithm is applied in the cost prediction of actual sintering production.
Abstract: This paper studies various training algorithms of BP neural network and proposes an improved conjugate gradient algorithm which combines conjugate gradient algorithm with inexact line search route based on generalized Curry principle. The proposed algorithm has global convergence, optimizes the learning steps using new line search rules and improves the convergence speed. The new algorithm is applied in the cost prediction of actual sintering production. Simulation results show that the algorithm has better convergence compared with traditional conjugate gradient algorithms. The MSE of prediction is 0.0098 and accuracy rate reaches 94.31%.

14 citations

Journal ArticleDOI
TL;DR: The extensive experimental analysis demonstrates that the proposed p2p learning model is efficient in learning and sharing for patient diagnosis and shows the potential impact under different network topologies, network sizes and the number of learning peers.

10 citations

References
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01 Jan 2002

9,314 citations


"A Classification Approach Based on ..." refers background in this paper

  • ...Classification is the process of finding a set of models that describe and distinguish data classes or concepts, for the purpose of being able to use the model to predict the class of objects whose class label is unknown [2]....

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Proceedings Article
02 Aug 1996
TL;DR: The KDD process and basic data mining algorithms are defined, links between data mining, knowledge discovery, and other related fields are described, and an analysis of challenges facing practitioners in the field is analyzed.
Abstract: This paper presents a first step towards a unifying framework for Knowledge Discovery in Databases. We describe links between data mining, knowledge discovery, and other related fields. We then define the KDD process and basic data mining algorithms, discuss application issues and conclude with an analysis of challenges facing practitioners in the field.

865 citations


"A Classification Approach Based on ..." refers background in this paper

  • ...Therefore, there is an urgent need for a new generation of computational techniques and tools to assist humans in extracting useful information (knowledge) from the rapidly growing volumes of data [1]....

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Journal ArticleDOI
TL;DR: Several complexity measures for Boolean functions are discussed: certificate complexity, sensitivity, block sensitivity, and the degree of a representing or approximating polynomial, and how they give bounds for the decision tree complexity of Boolean functions on deterministic, randomized, and quantum computers.

767 citations

Journal ArticleDOI
01 Jun 2003
TL;DR: Experimental results show that a single GSN can outperform both the SIANN and MLP networks.
Abstract: This article presents a new generalized feedforward neural network (GFNN) architecture for pattern classification and regression. The GFNN architecture uses as the basic computing unit a generalized shunting neuron (GSN) model, which includes as special cases the perceptron and the shunting inhibitory neuron. GSNs are capable of forming complex, nonlinear decision boundaries. This allows the GFNN architecture to easily learn some complex pattern classification problems. In this article the GFNNs are applied to several benchmark classification problems, and their performance is compared to the performances of SIANNs and multilayer perceptrons. Experimental results show that a single GSN can outperform both the SIANN and MLP networks.

104 citations