The Experts below are selected from a list of 75912 Experts worldwide ranked by ideXlab platform
Mingkun Zhang - One of the best experts on this subject based on the ideXlab platform.
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Privacy-Protected KNN Classification Algorithm Based on Negative Database
Advances in Natural Computation Fuzzy Systems and Knowledge Discovery, 2019Co-Authors: Hucheng Liao, Shihu Bu, Yu. Chen, Mingkun ZhangAbstract:Negative database (NDB) is a new type of information representation method that protects private data by storing data in the raw data complement set. The KNN Classification Algorithm is a classic Classification Algorithm, and the Euclidean distance formula is one of the most commonly used distance calculation formulas in Classification Algorithms. However, the distance calculation method for the existing KNN Classification Algorithm based on negative database is the one-hot coded Hamming distance formula. For this encoding method, when data set have many attributes, the length of the binary string becomes extremely long after encoding, thereby it increases the computational cost and complexity of the Classification Algorithm. In this paper, we proposed a KNN Classification Algorithm based on the Euclidean distance formula on the negative database, which is used to complete the Classification research under the premise of protecting data security. The experimental results show that the Algorithm in this paper achieves high Classification accuracy.
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ICNC-FSKD - Privacy-Protected KNN Classification Algorithm Based on Negative Database.
Advances in Natural Computation Fuzzy Systems and Knowledge Discovery, 2019Co-Authors: Hucheng Liao, Shihu Bu, Yu. Chen, Mingkun ZhangAbstract:Negative database (NDB) is a new type of information representation method that protects private data by storing data in the raw data complement set. The KNN Classification Algorithm is a classic Classification Algorithm, and the Euclidean distance formula is one of the most commonly used distance calculation formulas in Classification Algorithms. However, the distance calculation method for the existing KNN Classification Algorithm based on negative database is the one-hot coded Hamming distance formula. For this encoding method, when data set have many attributes, the length of the binary string becomes extremely long after encoding, thereby it increases the computational cost and complexity of the Classification Algorithm. In this paper, we proposed a KNN Classification Algorithm based on the Euclidean distance formula on the negative database, which is used to complete the Classification research under the premise of protecting data security. The experimental results show that the Algorithm in this paper achieves high Classification accuracy.
Zhang Hong-ke - One of the best experts on this subject based on the ideXlab platform.
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Image Classification Algorithm based on LTS-HD multi instance multi label RBF
2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2017Co-Authors: Min Jie, Zhang Hong-keAbstract:In order to improve the accuracy of image Classification and the robustness of the Algorithm, this paper proposes a Image Classification Algorithm based on LTS-HD(Least Trimmed Square Hausdorff) multi instance multi label RBF. The image Classification Algorithm based on the traditional Hausdorff distance has poor stability, and the Classification results are quite different. Therefore, the image Classification Algorithm based on the LTS Hausdorff overcomes the problems existing in the traditional Algorithm. In this paper, an improved K-Means clustering Algorithm which combines Canopy Algorithm and K-Means Algorithm not only optimizes the initial clustering center of K-Means Algorithm, but also reduces the time complexity of traditional K-Means Algorithm. The experimental results show that the proposed Algorithm can effectively resist the influence of noise and improve the accuracy of Classification.
Hucheng Liao - One of the best experts on this subject based on the ideXlab platform.
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Privacy-Protected KNN Classification Algorithm Based on Negative Database
Advances in Natural Computation Fuzzy Systems and Knowledge Discovery, 2019Co-Authors: Hucheng Liao, Shihu Bu, Yu. Chen, Mingkun ZhangAbstract:Negative database (NDB) is a new type of information representation method that protects private data by storing data in the raw data complement set. The KNN Classification Algorithm is a classic Classification Algorithm, and the Euclidean distance formula is one of the most commonly used distance calculation formulas in Classification Algorithms. However, the distance calculation method for the existing KNN Classification Algorithm based on negative database is the one-hot coded Hamming distance formula. For this encoding method, when data set have many attributes, the length of the binary string becomes extremely long after encoding, thereby it increases the computational cost and complexity of the Classification Algorithm. In this paper, we proposed a KNN Classification Algorithm based on the Euclidean distance formula on the negative database, which is used to complete the Classification research under the premise of protecting data security. The experimental results show that the Algorithm in this paper achieves high Classification accuracy.
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ICNC-FSKD - Privacy-Protected KNN Classification Algorithm Based on Negative Database.
Advances in Natural Computation Fuzzy Systems and Knowledge Discovery, 2019Co-Authors: Hucheng Liao, Shihu Bu, Yu. Chen, Mingkun ZhangAbstract:Negative database (NDB) is a new type of information representation method that protects private data by storing data in the raw data complement set. The KNN Classification Algorithm is a classic Classification Algorithm, and the Euclidean distance formula is one of the most commonly used distance calculation formulas in Classification Algorithms. However, the distance calculation method for the existing KNN Classification Algorithm based on negative database is the one-hot coded Hamming distance formula. For this encoding method, when data set have many attributes, the length of the binary string becomes extremely long after encoding, thereby it increases the computational cost and complexity of the Classification Algorithm. In this paper, we proposed a KNN Classification Algorithm based on the Euclidean distance formula on the negative database, which is used to complete the Classification research under the premise of protecting data security. The experimental results show that the Algorithm in this paper achieves high Classification accuracy.
Xia Sun - One of the best experts on this subject based on the ideXlab platform.
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Document Classification Algorithm based on kernel logistic regression
2010 2nd International Conference on Industrial and Information Systems, 2010Co-Authors: Ziqiang Wang, Xia SunAbstract:Document feature extraction and classifier selection are two key problems for document Classification approach. To effectively resolve the above two problems, a novel document Classification Algorithm is proposed by combining the merits of local fisher discriminant analysis and kernel logistic regression. Extensive experiments have been conducted, and the results demonstrate that the proposed Algorithm can offer better performance for document Classification in comparison with ordinary Classification Algorithms.
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Document Classification Algorithm Based on NPE and PSO
2009 International Conference on E-Business and Information System Security, 2009Co-Authors: Ziqiang Wang, Xia SunAbstract:With many potential applications in document management and Web searching, document Classification has recently gained more attention. To efficiently resolve this problem, an efficient document Classification Algorithm based on neighborhood preserving embedding (NPE) and particle swarm optimization (PSO) is proposed in this paper. The document features are first extracted by the NPE Algorithm, then the PSO classifier is used to classify the documents into semantically different classes. Experimental results show that the proposed Algorithm achieves much better performance than other related Classification Algorithms.
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An Efficient LDE-Based Document Classification Algorithm
2009 Pacific-Asia Conference on Circuits Communications and Systems, 2009Co-Authors: Ziqiang Wang, Xia SunAbstract:To efficiently cope with document Classification problem, an efficient document Classification Algorithm based on local discriminant embedding (LDE) and SVM classifier is proposed in this paper. The high-dimensional document space are first projected into the lower-dimensional feature space by using LDE Algorithm, the SVM classifier is then applied in the reduced document feature space. Extensive experiments show that the proposed Algorithm achieves much better performance than other traditional document Classification Algorithms.
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A MA-based Web document Classification Algorithm
2008 IEEE International Symposium on IT in Medicine and Education, 2008Co-Authors: Xia Sun, Ziqiang Wang, Dexian ZhangAbstract:The amount of online document has grown greatly in recent years due to the increase in popularity of the World Wide Web. Thus, developing an efficient document Classification method to automatically manipulate Web document is of great importance. A novel memetic Algorithm (MA)-based document Classification Algorithm is presented in this paper. The experimental results show that the proposed Algorithm achieves much better performance than other related Classification Algorithms.
Ziqiang Wang - One of the best experts on this subject based on the ideXlab platform.
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Document Classification Algorithm based on kernel logistic regression
2010 2nd International Conference on Industrial and Information Systems, 2010Co-Authors: Ziqiang Wang, Xia SunAbstract:Document feature extraction and classifier selection are two key problems for document Classification approach. To effectively resolve the above two problems, a novel document Classification Algorithm is proposed by combining the merits of local fisher discriminant analysis and kernel logistic regression. Extensive experiments have been conducted, and the results demonstrate that the proposed Algorithm can offer better performance for document Classification in comparison with ordinary Classification Algorithms.
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Document Classification Algorithm Using Kernel LPP
2009 International Conference on Computational Intelligence and Natural Computing, 2009Co-Authors: Ziqiang Wang, Xu QianAbstract:With the explosive increase in document data on the Internet, classifying documents from document database has become one of the hottest research fields.To efficiently deal with this problem, an efficient document Classification Algorithm based on kernel locality preserving projection (Kernel LPP) is presented in this paper. Experimental results show that the proposed Algorithm outperforms other related document Classification Algorithms.
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Document Classification Algorithm Based on NPE and PSO
2009 International Conference on E-Business and Information System Security, 2009Co-Authors: Ziqiang Wang, Xia SunAbstract:With many potential applications in document management and Web searching, document Classification has recently gained more attention. To efficiently resolve this problem, an efficient document Classification Algorithm based on neighborhood preserving embedding (NPE) and particle swarm optimization (PSO) is proposed in this paper. The document features are first extracted by the NPE Algorithm, then the PSO classifier is used to classify the documents into semantically different classes. Experimental results show that the proposed Algorithm achieves much better performance than other related Classification Algorithms.
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An Efficient LDE-Based Document Classification Algorithm
2009 Pacific-Asia Conference on Circuits Communications and Systems, 2009Co-Authors: Ziqiang Wang, Xia SunAbstract:To efficiently cope with document Classification problem, an efficient document Classification Algorithm based on local discriminant embedding (LDE) and SVM classifier is proposed in this paper. The high-dimensional document space are first projected into the lower-dimensional feature space by using LDE Algorithm, the SVM classifier is then applied in the reduced document feature space. Extensive experiments show that the proposed Algorithm achieves much better performance than other traditional document Classification Algorithms.
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A MA-based Web document Classification Algorithm
2008 IEEE International Symposium on IT in Medicine and Education, 2008Co-Authors: Xia Sun, Ziqiang Wang, Dexian ZhangAbstract:The amount of online document has grown greatly in recent years due to the increase in popularity of the World Wide Web. Thus, developing an efficient document Classification method to automatically manipulate Web document is of great importance. A novel memetic Algorithm (MA)-based document Classification Algorithm is presented in this paper. The experimental results show that the proposed Algorithm achieves much better performance than other related Classification Algorithms.
