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Zheng Chun-hong

Bio: Zheng Chun-hong is an academic researcher. The author has contributed to research in topics: Support vector machine & Ranking SVM. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Papers
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Journal Article
TL;DR: Experimental tests conducted on targets classification of 2-value remote sensing images demonstrate that the proposed approach can conduct automatic model selection with low error while providing significant savings in time.
Abstract: Support vector machine(SVM) has recently been proposed as a new effective learning machine for classification of remote sensing images.However,SVM often requires expensive design phases to choose adequate model parameters to attain high classification accuracy.A real-coded genetic algorithm(RGA) is used to automatically determine the model parameters for SVM, aiming at expediting the model selection process in SVM design with optimal generalization performance.Compared with the commonly used trial-and-error method,the proposed method is easier to implement.Furthermore,the generalization of the RGA-based SVM is much improved.Experimental tests conducted on targets classification of 2-value remote sensing images demonstrate that the proposed approach can conduct automatic model selection with low error while providing significant savings in time.

2 citations


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Journal ArticleDOI
TL;DR: A novel feature selection and classification method for hyperspectral images is proposed by combining the global optimization ability of particle swarm optimization (PSO) algorithm and the superior classification performance of a support vector machine (SVM).

45 citations

Journal ArticleDOI
01 Apr 2011
TL;DR: By inspiration of the granular evolutionary algorithm, a Granular Agent Evolutionary Classification (GAEC) algorithm for the classification task in data mining is proposed and test results show that the algorithm has a good classification prediction, and only need a small price for the training time.
Abstract: By inspiration of the granular evolutionary algorithm, a Granular Agent Evolutionary Classification (GAEC) algorithm for the classification task in data mining is proposed. The method uses the granular agent to denote the set of some examples that have similar attributions and the knowledge base guides the evolution of granular agent. Also some granular evolutionary operators are designed for classification problem. Assimilation operator, exchange operator, and differentiation operator reflect the competitive, cooperative and self-learning ability of agent, respectively. Finally, some classification rules are extracted from granular agents by some strategy to forecast the sort of new data. Empirical study contains UCI data sets, KDDCUP99 data sets and remote image recognition. The test results show that the algorithm has a good classification prediction, and only need a small price for the training time. In most UCI data sets, the performance of GAEC is better than G-NET, OCEC and C4.5, which have good performance. At the same time, some Gaussian White Noise attributes are added to these UCI data sets and the results show GACE has some anti-noise abilities. To test the scalability of GAEC, two functions along two dimensions, the number of training examples and the number of attributes are used. Also, GAEC are applied to some real world fields, intrusion detection system and remote sensing image recognition. The experiments for KDDCUP99 verify GAEC has capability to deal with massive data in real world and good predicting capability for unknown type data. At last, the accuracy rate of GAEC is also good for the remote sensing image recognition.

9 citations