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Dexian Zhang

Bio: Dexian Zhang is an academic researcher from Henan University of Technology. The author has contributed to research in topics: Support vector machine & Particle swarm optimization. The author has an hindex of 10, co-authored 51 publications receiving 370 citations.

Papers published on a yearly basis

Papers
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Proceedings ArticleDOI
01 Aug 2006
TL;DR: An optimal SVM algorithm for text classification via multiple optimal strategies is proposed and the experimental results indicate that the proposed optimal classification algorithm yields much better performance than other conventional algorithms.
Abstract: The goal of a text classification system is to determine whether a given document belongs to which of the predefined categories. An optimal SVM algorithm for text classification via multiple optimal strategies is proposed in this paper. The experimental results indicate that the proposed optimal classification algorithm yields much better performance than other conventional algorithms.

71 citations

Book ChapterDOI
17 Nov 2009
TL;DR: To efficiently mine the classification rule from databases, a novel classification rule mining algorithm based on particle swarm optimization (PSO) was proposed and results show that the proposed algorithm achieved higher predictive accuracy and much smaller rule list than other classification algorithm.
Abstract: Classification rule mining is one of the important problems in the emerging field of data mining which is aimed at finding a small set of rules from the training data set with predetermined targets. To efficiently mine the classification rule from databases, a novel classification rule mining algorithm based on particle swarm optimization (PSO) was proposed. The experimental results show that the proposed algorithm achieved higher predictive accuracy and much smaller rule list than other classification algorithm.

47 citations

Book ChapterDOI
14 Sep 2007
TL;DR: A novel blind watermark extracting scheme using the Discrete Wavelet Transform (DWT) and Particle Swarm Optimization (PSO) algorithm that results in an almost invisible difference between the watermarked image and the original image, and is robust to common image processing operations and JPEG lossy compression.
Abstract: In this paper, a novel blind watermark extracting scheme using the Discrete Wavelet Transform (DWT) and Particle Swarm Optimization (PSO) algorithm is introduced. The watermark is embedded to the discrete multiwavelet transform (DMT) coefficients larger than some threshold values, and watermark extraction is efficiently performed via particle swarm optimization algorithm.The experimental results show that the proposed watermarking scheme results in an almost invisible difference between the watermarked image and the original image, and is robust to common image processing operations and JPEG lossy compression.

45 citations

Patent
29 Sep 2010
TL;DR: In this paper, a grain cabin capacity information wireless monitoring system, monitoring method and networking method, belonging to the wireless communication and networking technical field, is presented. But the system can adapt to sensor output signal meeting industrial standard, is compatible with heterogeneous sensor and has strong generality.
Abstract: The invention relates to a grain cabin capacity information wireless monitoring system, monitoring method and networking method, belonging to the wireless communication and networking technical field. The grain tank capacity information wireless monitoring system of the invention comprises a cabin grain condition data acquisition subsystem based on CAN bus, a cabin grain condition data transmission subsystem based on Zigbee, a cabin grain condition data transmission subsystem based on GPRS and a center server subsystem; wherein the wire data acquisition network based on CAN bus is combined with the wireless data transmission network, thus not only playing the advantages of simple power supply and high reliable data transmission of the wired network, but also playing the advantages of no wiring, low cost and low power consumption of the wireless transmission network; and ZigBee short distance wireless communication is combined with GPRS long distance wireless communication, the advantages can be mutually compensated, and cabin grain condition monitoring in long-distance range is realized. The system can adapt to sensor output signal meeting industrial standard, is compatible with heterogeneous sensor and has strong generality.

30 citations

Proceedings ArticleDOI
30 Jul 2007
TL;DR: Comparison between the proposed PSO-based algorithm for document classification and other conventional document classification algorithms is conducted and results indicate that the proposed algorithm yields much better performance than other conventional algorithms.
Abstract: Due to the exponential growth of documents in the Internet and the emergent need to organize them, the automatic document classification has received an ever-increased attention in the recent years. The particle swarm optimization (PSO) algorithm, new to the document classification community, is a robust stochastic evolutionary algorithm based on the movement and intelligence of swarms. In this paper, a PSO-based algorithm for document classification is presented. Comparison between our method and other conventional document classification algorithms is conducted on Reuter and TREC corpora. The experimental results indicate that our proposed algorithm yields much better performance than other conventional algorithms.

21 citations


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Book
01 Jan 1975
TL;DR: The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval, which I think is one of the most interesting and active areas of research in information retrieval.
Abstract: The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval. This chapter has been included because I think this is one of the most interesting and active areas of research in information retrieval. There are still many problems to be solved so I hope that this particular chapter will be of some help to those who want to advance the state of knowledge in this area. All the other chapters have been updated by including some of the more recent work on the topics covered. In preparing this new edition I have benefited from discussions with Bruce Croft, The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. Some of the chapters, particularly Chapter 6 * , make simple use of a little advanced mathematics. However, the necessary mathematical tools can be easily mastered from numerous mathematical texts that now exist and, in any case, references have been given where the mathematics occur. I had to face the problem of balancing clarity of exposition with density of references. I was tempted to give large numbers of references but was afraid they would have destroyed the continuity of the text. I have tried to steer a middle course and not compete with the Annual Review of Information Science and Technology. Normally one is encouraged to cite only works that have been published in some readily accessible form, such as a book or periodical. Unfortunately, much of the interesting work in IR is contained in technical reports and Ph.D. theses. For example, most the work done on the SMART system at Cornell is available only in reports. Luckily many of these are now available through the National Technical Information Service (U.S.) and University Microfilms (U.K.). I have not avoided using these sources although if the same material is accessible more readily in some other form I have given it preference. I should like to acknowledge my considerable debt to many people and institutions that have helped me. Let me say first that they are responsible for many of the ideas in this book but that only I wish to be held responsible. My greatest debt is to Karen Sparck Jones who taught me to research information retrieval as an experimental science. Nick Jardine and Robin …

822 citations

Journal ArticleDOI
TL;DR: This paper provides a review of the theory and methods of document classification and text mining, focusing on the existing techniques and methodologies, focused mainly on text representation and machine learning techniques.
Abstract: With the increasing availability of electronic documents and the rapid growth of the World Wide Web, the task of automatic categorization of documents became the key method for organizing the information and know- ledge discovery. Proper classification of e-documents, online news, blogs, e-mails and digital libraries need text mining, machine learning and natural language processing tech- niques to get meaningful knowledge. The aim of this paper is to highlight the important techniques and methodologies that are employed in text documents classification, while at the same time making awareness of some of the interesting challenges that remain to be solved, focused mainly on text representation and machine learning techniques. This paper provides a review of the theory and methods of document classification and text mining, focusing on the existing litera- ture.

546 citations

Journal Article
Shi Bing1
TL;DR: Text categorization-assignment of natural language texts to one or more predefined categories based on their content-is an important component in many information organization and management tasks.
Abstract: Text categorization-assignment of natural language texts to one or more predefined categories based on their content-is an important component in many information organization and management tasks.Different automatic learning algorithms for text categori-zation have different classification accuracy.Very accurate text classifiers can be learned automatically from training examples.

384 citations

Journal ArticleDOI
TL;DR: This paper investigates the growth of literature in SI and its algorithms, particularly Particle Swarm Optimization (PSO), and provides a thorough literature overview focusing on some of the most cited techniques that have been used for PSO-based data clustering.
Abstract: Optimization based pattern discovery has emerged as an important field in knowledge discovery and data mining (KDD), and has been used to enhance the efficiency and accuracy of clustering, classification, association rules and outlier detection. Cluster analysis, which identifies groups of similar data items in large datasets, is one of its recent beneficiaries. The increasing complexity and large amounts of data in the datasets have seen data clustering emerge as a popular focus for the application of optimization based techniques. Different optimization techniques have been applied to investigate the optimal solution for clustering problems. Swarm intelligence (SI) is one such optimization technique whose algorithms have successfully been demonstrated as solutions for different data clustering domains. In this paper we investigate the growth of literature in SI and its algorithms, particularly Particle Swarm Optimization (PSO). This paper makes two major contributions. Firstly, it provides a thorough literature overview focusing on some of the most cited techniques that have been used for PSO-based data clustering. Secondly, we analyze the reported results and highlight the performance of different techniques against contemporary clustering techniques. We also provide an brief overview of our PSO-based hierarchical clustering approach (HPSO-clustering) and compare the results with traditional hierarchical agglomerative clustering (HAC), K-means, and PSO clustering.

191 citations

Journal ArticleDOI
TL;DR: The generic strategy for automatic text classification is explained and existing solutions to major issues such as dealing with unstructured text, handling large number of attributes and selecting a machine learning technique appropriate to the text-classification application are surveyed.
Abstract: Automatic Text Classification is a semi-supervised machine learning task that automatically assigns a given document to a set of pre-defined categories based on its textual content and extracted features. Automatic Text Classification has important applications in content management, contextual search, opinion mining, product review analysis, spam filtering and text sentiment mining. This paper explains the generic strategy for automatic text classification and surveys existing solutions to major issues such as dealing with unstructured text, handling large number of attributes and selecting a machine learning technique appropriate to the text-classification application.

152 citations