The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Zhimin Chen - One of the best experts on this subject based on the ideXlab platform.
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FSKD - Land Evaluation algorithms based on simplified fuzzy classification association rules and grouping fuzzy decision
2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010Co-Authors: Ting Li, Jingfeng Yang, Zhimin ChenAbstract:To improve the intelligibility and efficiency of knowledge expression for the Land Evaluation, a Land Evaluation method combining simplified fuzzy classification association rules with fuzzy decision is proposed in this paper. To reduce the complexity of the Land Evaluation models and improve the efficiency and intelligibility of fuzzy classification association rules further, an algorithm to eliminate redundant rules for obtaining the simplified fuzzy classification association rules is presented. In addition, considering the challenge of a few samples that are difficult to classify the process of fuzzy decision, an iterative algorithm for grouping fuzzy decision for datasets is discussed. The results of experiments demonstrate that by using only 32 simplified fuzzy classification association rules, accuracy of area of Land Evaluation can reach 92.2835 percent. It provides a higher precision with the accuracy improved by 5.0039%, comparing with the results of the method combining 32 original fuzzy classification association rules with fuzzy decision when minimum support is 0.005.1
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Land Evaluation algorithms based on simplified fuzzy classification association rules and grouping fuzzy decision
2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010Co-Authors: Ting Li, Jingfeng Yang, Zhimin ChenAbstract:To improve the intelligibility and efficiency of knowledge expression for the Land Evaluation, a Land Evaluation method combining simplified fuzzy classification association rules with fuzzy decision is proposed in this paper. To reduce the complexity of the Land Evaluation models and improve the efficiency and intelligibility of fuzzy classification association rules further, an algorithm to eliminate redundant rules for obtaining the simplified fuzzy classification association rules is presented. In addition, considering the challenge of a few samples that are difficult to classify the process of fuzzy decision, an iterative algorithm for grouping fuzzy decision for datasets is discussed. The results of experiments demonstrate that by using only 32 simplified fuzzy classification association rules, accuracy of area of Land Evaluation can reach 92.2835 percent. It provides a higher precision with the accuracy improved by 5.0039%, comparing with the results of the method combining 32 original fuzzy classification association rules with fuzzy decision when minimum support is 0.005.
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Semi-supervised Learning Algorithm Based on Simplified Association Rules Combining with k-mean and Its Application in Land Evaluation
2008 Fourth International Conference on Natural Computation, 2008Co-Authors: Ting Li, Jingfeng Yang, Xiaoqin Peng, Zhimin ChenAbstract:In order to construct intelligible and effective Land Evaluation classifier, a semi-supervised learning algorithm constructed by utilizing simplified association rules combining with k-mean clustering algorithm is proposed in this paper. To reduce the complexity of the Land Evaluation models and improve the efficiency and intelligibility of association rules further, an algorithm to eliminate redundant rules for obtaining the simplified association rules is presented. Experimental results of Guangdong Province Land resource demonstrate that, by only using 500 training samples chosen randomly, 89.5143% correct area rate of Land Evaluation could be obtained by the semi-supervised learning algorithm. It provides a higher precision with the accuracy improved by 14.3484%, comparing with the results of the method k-mean and 7.1159% comparing with the results of the method support vector machine in the same condition.
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FSKD (1) - Land Evaluation Based on Semi-supervised Learning Algorithm
Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), 2007Co-Authors: Zhimin Chen, Jingfeng Yang, Jiaqi Zhang, Qiang ChenAbstract:In order to classify tremendous amount of unlabeled samples accurately and efficiently, a Land Evaluation method based on semi-supervised learning algorithm is proposed in this paper. Extracting Land Evaluation association rules by training a small amount of labeled samples as the supervised information, combining with the Chameleon algorithm as the unsupervised method, the method clusters a great amount of unlabeled samples, which takes full advantage of high accuracy of supervised learning classification, reduces the complexity of clustering process, and improves the facility, interpretability and accuracy for the Land Evaluation model. Experimental results of Guangdong Province Land resource demonstrate that, by only using 300 training samples chosen randomly, 94.4184% correct area rate of Land Evaluation could be obtained by the semi-supervised learning algorithm. It provides a higher precision with the accuracy improved by 4.9041%, comparing with the results of the method clustering in the same condition.
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FSKD (3) - Land Evaluation Method Based on Simplified Fuzzy Classification Association Rules
Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), 2007Co-Authors: Jingfeng Yang, Zhimin Chen, Yueming Hu, Qiang ChenAbstract:To improve the intelligibility and efficiency of the Land Evaluation knowledge, a Land Evaluation method integrating simplified fuzzy classification association rules with fuzzy decision is proposed in this paper. Moreover, to reduce the complexity of the Land Evaluation models, an algorithm to eliminate redundant rules for obtaining the simplified fuzzy classification association rules is presented. The results of experiments demonstrate that Land resource can be evaluated more accurately by using simplified fuzzy classification association rules, comparing with the results of the method combining the same number of original fuzzy classification association rules.
Qiang Chen - One of the best experts on this subject based on the ideXlab platform.
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FSKD (1) - Land Evaluation Based on Semi-supervised Learning Algorithm
Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), 2007Co-Authors: Zhimin Chen, Jingfeng Yang, Jiaqi Zhang, Qiang ChenAbstract:In order to classify tremendous amount of unlabeled samples accurately and efficiently, a Land Evaluation method based on semi-supervised learning algorithm is proposed in this paper. Extracting Land Evaluation association rules by training a small amount of labeled samples as the supervised information, combining with the Chameleon algorithm as the unsupervised method, the method clusters a great amount of unlabeled samples, which takes full advantage of high accuracy of supervised learning classification, reduces the complexity of clustering process, and improves the facility, interpretability and accuracy for the Land Evaluation model. Experimental results of Guangdong Province Land resource demonstrate that, by only using 300 training samples chosen randomly, 94.4184% correct area rate of Land Evaluation could be obtained by the semi-supervised learning algorithm. It provides a higher precision with the accuracy improved by 4.9041%, comparing with the results of the method clustering in the same condition.
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FSKD (3) - Land Evaluation Method Based on Simplified Fuzzy Classification Association Rules
Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), 2007Co-Authors: Jingfeng Yang, Zhimin Chen, Yueming Hu, Qiang ChenAbstract:To improve the intelligibility and efficiency of the Land Evaluation knowledge, a Land Evaluation method integrating simplified fuzzy classification association rules with fuzzy decision is proposed in this paper. Moreover, to reduce the complexity of the Land Evaluation models, an algorithm to eliminate redundant rules for obtaining the simplified fuzzy classification association rules is presented. The results of experiments demonstrate that Land resource can be evaluated more accurately by using simplified fuzzy classification association rules, comparing with the results of the method combining the same number of original fuzzy classification association rules.
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Land Evaluation Based on Semi-supervised Learning Algorithm
Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), 2007Co-Authors: Zhimin Chen, Jingfeng Yang, Jiaqi Zhang, Qiang ChenAbstract:In order to classify tremendous amount of unlabeled samples accurately and efficiently, a Land Evaluation method based on semi-supervised learning algorithm is proposed in this paper. Extracting Land Evaluation association rules by training a small amount of labeled samples as the supervised information, combining with the Chameleon algorithm as the unsupervised method, the method clusters a great amount of unlabeled samples, which takes full advantage of high accuracy of supervised learning classification, reduces the complexity of clustering process, and improves the facility, interpretability and accuracy for the Land Evaluation model. Experimental results of Guangdong Province Land resource demonstrate that, by only using 300 training samples chosen randomly, 94.4184% correct area rate of Land Evaluation could be obtained by the semi-supervised learning algorithm. It provides a higher precision with the accuracy improved by 4.9041%, comparing with the results of the method clustering in the same condition.
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Land Evaluation Method Based on Simplified Fuzzy Classification Association Rules
Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), 2007Co-Authors: Jingfeng Yang, Zhimin Chen, Yueming Hu, Qiang ChenAbstract:To improve the intelligibility and efficiency of the Land Evaluation knowledge, a Land Evaluation method integrating simplified fuzzy classification association rules with fuzzy decision is proposed in this paper. Moreover, to reduce the complexity of the Land Evaluation models, an algorithm to eliminate redundant rules for obtaining the simplified fuzzy classification association rules is presented. The results of experiments demonstrate that Land resource can be evaluated more accurately by using simplified fuzzy classification association rules, comparing with the results of the method combining the same number of original fuzzy classification association rules.
Jingfeng Yang - One of the best experts on this subject based on the ideXlab platform.
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FSKD - Land Evaluation algorithms based on simplified fuzzy classification association rules and grouping fuzzy decision
2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010Co-Authors: Ting Li, Jingfeng Yang, Zhimin ChenAbstract:To improve the intelligibility and efficiency of knowledge expression for the Land Evaluation, a Land Evaluation method combining simplified fuzzy classification association rules with fuzzy decision is proposed in this paper. To reduce the complexity of the Land Evaluation models and improve the efficiency and intelligibility of fuzzy classification association rules further, an algorithm to eliminate redundant rules for obtaining the simplified fuzzy classification association rules is presented. In addition, considering the challenge of a few samples that are difficult to classify the process of fuzzy decision, an iterative algorithm for grouping fuzzy decision for datasets is discussed. The results of experiments demonstrate that by using only 32 simplified fuzzy classification association rules, accuracy of area of Land Evaluation can reach 92.2835 percent. It provides a higher precision with the accuracy improved by 5.0039%, comparing with the results of the method combining 32 original fuzzy classification association rules with fuzzy decision when minimum support is 0.005.1
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Land Evaluation algorithms based on simplified fuzzy classification association rules and grouping fuzzy decision
2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010Co-Authors: Ting Li, Jingfeng Yang, Zhimin ChenAbstract:To improve the intelligibility and efficiency of knowledge expression for the Land Evaluation, a Land Evaluation method combining simplified fuzzy classification association rules with fuzzy decision is proposed in this paper. To reduce the complexity of the Land Evaluation models and improve the efficiency and intelligibility of fuzzy classification association rules further, an algorithm to eliminate redundant rules for obtaining the simplified fuzzy classification association rules is presented. In addition, considering the challenge of a few samples that are difficult to classify the process of fuzzy decision, an iterative algorithm for grouping fuzzy decision for datasets is discussed. The results of experiments demonstrate that by using only 32 simplified fuzzy classification association rules, accuracy of area of Land Evaluation can reach 92.2835 percent. It provides a higher precision with the accuracy improved by 5.0039%, comparing with the results of the method combining 32 original fuzzy classification association rules with fuzzy decision when minimum support is 0.005.
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Semi-supervised Learning Algorithm Based on Simplified Association Rules Combining with k-mean and Its Application in Land Evaluation
2008 Fourth International Conference on Natural Computation, 2008Co-Authors: Ting Li, Jingfeng Yang, Xiaoqin Peng, Zhimin ChenAbstract:In order to construct intelligible and effective Land Evaluation classifier, a semi-supervised learning algorithm constructed by utilizing simplified association rules combining with k-mean clustering algorithm is proposed in this paper. To reduce the complexity of the Land Evaluation models and improve the efficiency and intelligibility of association rules further, an algorithm to eliminate redundant rules for obtaining the simplified association rules is presented. Experimental results of Guangdong Province Land resource demonstrate that, by only using 500 training samples chosen randomly, 89.5143% correct area rate of Land Evaluation could be obtained by the semi-supervised learning algorithm. It provides a higher precision with the accuracy improved by 14.3484%, comparing with the results of the method k-mean and 7.1159% comparing with the results of the method support vector machine in the same condition.
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FSKD (1) - Land Evaluation Based on Semi-supervised Learning Algorithm
Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), 2007Co-Authors: Zhimin Chen, Jingfeng Yang, Jiaqi Zhang, Qiang ChenAbstract:In order to classify tremendous amount of unlabeled samples accurately and efficiently, a Land Evaluation method based on semi-supervised learning algorithm is proposed in this paper. Extracting Land Evaluation association rules by training a small amount of labeled samples as the supervised information, combining with the Chameleon algorithm as the unsupervised method, the method clusters a great amount of unlabeled samples, which takes full advantage of high accuracy of supervised learning classification, reduces the complexity of clustering process, and improves the facility, interpretability and accuracy for the Land Evaluation model. Experimental results of Guangdong Province Land resource demonstrate that, by only using 300 training samples chosen randomly, 94.4184% correct area rate of Land Evaluation could be obtained by the semi-supervised learning algorithm. It provides a higher precision with the accuracy improved by 4.9041%, comparing with the results of the method clustering in the same condition.
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FSKD (3) - Land Evaluation Method Based on Simplified Fuzzy Classification Association Rules
Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), 2007Co-Authors: Jingfeng Yang, Zhimin Chen, Yueming Hu, Qiang ChenAbstract:To improve the intelligibility and efficiency of the Land Evaluation knowledge, a Land Evaluation method integrating simplified fuzzy classification association rules with fuzzy decision is proposed in this paper. Moreover, to reduce the complexity of the Land Evaluation models, an algorithm to eliminate redundant rules for obtaining the simplified fuzzy classification association rules is presented. The results of experiments demonstrate that Land resource can be evaluated more accurately by using simplified fuzzy classification association rules, comparing with the results of the method combining the same number of original fuzzy classification association rules.
Ting Li - One of the best experts on this subject based on the ideXlab platform.
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FSKD - Land Evaluation algorithms based on simplified fuzzy classification association rules and grouping fuzzy decision
2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010Co-Authors: Ting Li, Jingfeng Yang, Zhimin ChenAbstract:To improve the intelligibility and efficiency of knowledge expression for the Land Evaluation, a Land Evaluation method combining simplified fuzzy classification association rules with fuzzy decision is proposed in this paper. To reduce the complexity of the Land Evaluation models and improve the efficiency and intelligibility of fuzzy classification association rules further, an algorithm to eliminate redundant rules for obtaining the simplified fuzzy classification association rules is presented. In addition, considering the challenge of a few samples that are difficult to classify the process of fuzzy decision, an iterative algorithm for grouping fuzzy decision for datasets is discussed. The results of experiments demonstrate that by using only 32 simplified fuzzy classification association rules, accuracy of area of Land Evaluation can reach 92.2835 percent. It provides a higher precision with the accuracy improved by 5.0039%, comparing with the results of the method combining 32 original fuzzy classification association rules with fuzzy decision when minimum support is 0.005.1
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Land Evaluation algorithms based on simplified fuzzy classification association rules and grouping fuzzy decision
2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010Co-Authors: Ting Li, Jingfeng Yang, Zhimin ChenAbstract:To improve the intelligibility and efficiency of knowledge expression for the Land Evaluation, a Land Evaluation method combining simplified fuzzy classification association rules with fuzzy decision is proposed in this paper. To reduce the complexity of the Land Evaluation models and improve the efficiency and intelligibility of fuzzy classification association rules further, an algorithm to eliminate redundant rules for obtaining the simplified fuzzy classification association rules is presented. In addition, considering the challenge of a few samples that are difficult to classify the process of fuzzy decision, an iterative algorithm for grouping fuzzy decision for datasets is discussed. The results of experiments demonstrate that by using only 32 simplified fuzzy classification association rules, accuracy of area of Land Evaluation can reach 92.2835 percent. It provides a higher precision with the accuracy improved by 5.0039%, comparing with the results of the method combining 32 original fuzzy classification association rules with fuzzy decision when minimum support is 0.005.
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Semi-supervised Learning Algorithm Based on Simplified Association Rules Combining with k-mean and Its Application in Land Evaluation
2008 Fourth International Conference on Natural Computation, 2008Co-Authors: Ting Li, Jingfeng Yang, Xiaoqin Peng, Zhimin ChenAbstract:In order to construct intelligible and effective Land Evaluation classifier, a semi-supervised learning algorithm constructed by utilizing simplified association rules combining with k-mean clustering algorithm is proposed in this paper. To reduce the complexity of the Land Evaluation models and improve the efficiency and intelligibility of association rules further, an algorithm to eliminate redundant rules for obtaining the simplified association rules is presented. Experimental results of Guangdong Province Land resource demonstrate that, by only using 500 training samples chosen randomly, 89.5143% correct area rate of Land Evaluation could be obtained by the semi-supervised learning algorithm. It provides a higher precision with the accuracy improved by 14.3484%, comparing with the results of the method k-mean and 7.1159% comparing with the results of the method support vector machine in the same condition.
Diego De La Rosa - One of the best experts on this subject based on the ideXlab platform.
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Soil quality Evaluation and monitoring based on Land Evaluation
Land Degradation and Development, 2005Co-Authors: Diego De La RosaAbstract:Soil quality Evaluation is the process of predicting the capacity of a soil to function. Due to the many possible soil functions, simple measuring of an individual soil parameter is not sufficient. These soil-quality parameters or indicators are grouped in physical, chemical and biological components. Land Evaluation, which tries to predict Land behaviour for each particular use, is not the same as soil-quality assessment, basically because the biological parameters of the soil are not considered by Land Evaluation. However, the process of evaluating soil is not new, and agro-ecological Land Evaluation has much to offer in this sense. A state-of-the-art review of soil quality Evaluation and monitoring is presented in this paper, focusing on the possibilities of applying the accumulated knowledge from past studies of Land Evaluation. An agro-ecological approach is suggested to facilitate the monumental task of relating the nearly infinite list of soil quality indicators to the numerous soil functions, such as an application of MicroLEIS DSS to soil quality. Copyright (c) 2005 John Wiley & Sons, Ltd.
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Microleis 3.2: a set of computer programs, statistical models and expert systems for Land Evaluation
Soil Responses to Climate Change, 1994Co-Authors: Diego De La RosaAbstract:Although increasing consideration is being given to agricultural diversification and to lower input agriculture, it is still important to identify optimum Land use systems for resource sustainability and environmental quality. Land Evaluation makes it possible to use Land according to its potential. During the last few years, increasing application of information technology to Land Evaluation procedures has led to the development of Land Evaluation information systems. For these computerised applications, the microcomputer (PC platform) has become an essential tool.
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microleis a microcomputer based mediterranean Land Evaluation information system
Soil Use and Management, 1992Co-Authors: Diego De La Rosa, J A Moreno, Luis V Garcia, J AlmorzaAbstract:Abstract. A computer-based Land Evaluation information system (MicroLEIS) was developed for optimal use of agricultural and forestry Land systems under Mediterranean conditions. Through an interactive procedure several Land capability, suitability and yield prediction methods may be applied. The system addresses Land Evaluation at reconnaissance, semi-detailed and detailed scales in an interrelated manner. Biophysical Land Evaluation methods are incorporated using empirical, scale-appropriate models, which range from purely qualitative (reconnaissance) through semi-quantitative (semi-detailed) to quantitative (detailed). This software is helpful for teaching, research and development, predicting appropriate agroforestry Land uses. Its use is illustrated by an example. MicroLEIS runs on IBM PC, XT, AT, or a compatible microcomputer with at least 128 kilobytes of RAM and a PC-DOS or MS-DOS version 2.0 or later operating system. The software package on double or high density diskettes can be obtained from the first author.
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MicroLEIS: A microcomputer‐based Mediterranean Land Evaluation information system
Soil Use and Management, 1992Co-Authors: Diego De La Rosa, J A Moreno, Luis V Garcia, J AlmorzaAbstract:Abstract. A computer-based Land Evaluation information system (MicroLEIS) was developed for optimal use of agricultural and forestry Land systems under Mediterranean conditions. Through an interactive procedure several Land capability, suitability and yield prediction methods may be applied. The system addresses Land Evaluation at reconnaissance, semi-detailed and detailed scales in an interrelated manner. Biophysical Land Evaluation methods are incorporated using empirical, scale-appropriate models, which range from purely qualitative (reconnaissance) through semi-quantitative (semi-detailed) to quantitative (detailed). This software is helpful for teaching, research and development, predicting appropriate agroforestry Land uses. Its use is illustrated by an example. MicroLEIS runs on IBM PC, XT, AT, or a compatible microcomputer with at least 128 kilobytes of RAM and a PC-DOS or MS-DOS version 2.0 or later operating system. The software package on double or high density diskettes can be obtained from the first author.
