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Web page classification based on heterogeneous features and a combination of multiple classifiers Research Articles

Li Deng, Xin Du, Ji-zhong Shen,jzshen@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7,   Pages 963-1118 doi: 10.1631/FITEE.1900240

Abstract: Precise can be achieved by evaluating features of web pages, and the structural features of web pages are effective complements to their textual features. Various classifiers have different characteristics, and multiple classifiers can be combined to allow classifiers to complement one another. In this study, a method based on heterogeneous features and a combination of multiple classifiers is proposed. Different from computing the frequency of HTML tags, we exploit the tree-like structure of HTML tags to characterize the structural features of a web page. Heterogeneous textual features and the proposed tree-like structural features are converted into vectors and fused. Confidence is proposed here as a criterion to compare the classification results of different classifiers by calculating the classification accuracy of a set of samples. Multiple classifiers are combined based on confidence with different decision strategies, such as voting, confidence comparison, and direct output, to give the final classification results. Experimental results demonstrate that on the Amazon dataset, 7-web-genres dataset, and DMOZ dataset, the accuracies are increased to 94.2%, 95.4%, and 95.7%, respectively. The fusion of the textual features with the proposed structural features is a comprehensive approach, and the accuracy is higher than that when using only textual features. At the same time, the accuracy of the is improved by combining multiple classifiers, and is higher than those of the related algorithms.

Keywords： 网页分类；网页特征；分类器组合    

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A Rough Fuzzy Neural Classifier

Zeng Huanglin,Wang Xiao

Strategic Study of CAE 2003, Volume 5, Issue 12,   Pages 60-65

Abstract:

In this paper, the concepts of rough sets are used to define equivalence classes encoding input data sets, and eliminate redundant or insignificant attributes in data sets so that to reduce the complexity of system construction. In order to deal with ill-defined or real experimental data, an input object is represented as a fuzzy variable by fuzzy membership function, and the significant factor of the input feature corresponding to output pattern classification is incorporated to constitute a fuzzy inference so that to enhance nonlinear mapping classification. A new kind of rough fuzzy neural classifier and a learning algorithm with LSE are proposed in this paper. A integration of the merits of fuzzy and neural network technologies can not only accommodate overlapping classification and therefore increase the performance of nonlinear mapping classification, but ensure more efficiently to handle real life ambiguous and changing situations and to achieve tractability, robustness, and low-cost solutions.

Keywords： fuzzy sets     rough sets     neural networks     pattern classification    

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Representation learning via a semi-supervised stacked distance autoencoder for image classification Research Articles

Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang,jliang@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7,   Pages 963-1118 doi: 10.1631/FITEE.1900116

Abstract: is an important application of deep learning. In a typical classification task, the classification accuracy is strongly related to the features that are extracted via deep learning methods. An is a special type of , often used for dimensionality reduction and feature extraction. The proposed method is based on the traditional , incorporating the “distance” information between samples from different categories. The model is called a semi-supervised distance . Each layer is first pre-trained in an unsupervised manner. In the subsequent supervised training, the optimized parameters are set as the initial values. To obtain more suitable features, we use a stacked model to replace the basic structure with a single hidden layer. A series of experiments are carried out to test the performance of different models on several datasets, including the MNIST dataset, street view house numbers (SVHN) dataset, German traffic sign recognition benchmark (GTSRB), and CIFAR-10 dataset. The proposed semi-supervised distance method is compared with the traditional , sparse , and supervised . Experimental results verify the effectiveness of the proposed model.

Keywords： 自动编码器；图像分类；半监督学习；神经网络    

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A new constrained maximum margin approach to discriminative learning of Bayesian classifiers None

Ke GUO, Xia-bi LIU, Lun-hao GUO, Zong-jie LI, Zeng-min GENG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 5,   Pages 639-650 doi: 10.1631/FITEE.1700007

Abstract: We propose a novel discriminative learning approach for Bayesian pattern classification, called ‘constrained maximum margin (CMM)’. We define the margin between two classes as the difference between the minimum decision value for positive samples and the maximum decision value for negative samples. The learning problem is to maximize the margin under the constraint that each training pattern is classified correctly. This nonlinear programming problem is solved using the sequential unconstrained minimization technique. We applied the proposed CMM approach to learn Bayesian classifiers based on Gaussian mixture models, and conducted the experiments on 10 UCI datasets. The performance of our approach was compared with those of the expectation-maximization algorithm, the support vector machine, and other state-of-the-art approaches. The experimental results demonstrated the effectiveness of our approach.

Keywords： Discriminative learning     Statistical modeling     Bayesian pattern classifiers     Gaussian mixture models     UCI datasets    

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Joint tracking and classification of extended targets with complex shapes Research Articles

Liping Wang, Ronghui Zhan, Yuan Huang, Jun Zhang, Zhaowen Zhuang,zhanrh@nudt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 6,   Pages 839-861 doi: 10.1631/FITEE.2000061

Abstract: This paper addresses the problem of (JTC) of a single with a complex shape. To describe this complex shape, the spatial extent state is first modeled by star-convex shape via a (RHM), and then used as feature information for target classification. The target state is modeled by two vectors to alleviate the influence of the high-dimensional state space and the severely nonlinear observation model on target state estimation, while the Euclidean distance metric of the normalized is applied to obtain the analytical solution of the updated class probability. Consequently, the resulting method is called the “JTC-RHM method.” Besides, the proposed JTC-RHM is integrated into a framework to solve the JTC of a single in the presence of detection uncertainty and clutter, resulting in a JTC-RHM-Ber filter. Specifically, the recursive expressions of this filter are derived. Simulations indicate that: (1) the proposed JTC-RHM method can classify the targets with complex shapes and similar sizes more correctly, compared with the JTC method based on the random matrix model; (2) the proposed method performs better in target state estimation than the star-convex RHM based tracking method; (3) the proposed JTC-RHM-Ber filter has a promising performance in state detection and estimation, and can achieve target classification correctly.

Keywords： 扩展目标；傅里叶描述子；联合跟踪与分类；随机超曲面模型；伯努利滤波器    

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NEHASH: high-concurrency extendible hashing for non-volatile memory Research Article

Qiankun WANG, Xingchen LI, Bingzhe WU, Ke YANG, Wei HU, Guangyu SUN, Yuchao YANG,gsun@pku.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 5,   Pages 731-741 doi: 10.1631/FITEE.2200463

Abstract: The problem (COP), which aims to find the optimal solution in discrete space, is fundamental in various fields. Unfortunately, many COPs are NP-complete, and require much more time to solve as the problem scale increases. Troubled by this, researchers may prefer fast methods even if they are not exact, so approximation algorithms, heuristic algorithms, and machine learning have been proposed. Some works proposed (CSA) based on the Hopfield neural network and did a good job. However, CSA is not something that current general-purpose processors can handle easily, and there is no special hardware for it. To efficiently perform CSA, we propose a software and hardware co-design. In software, we quantize the weight and output using appropriate bit widths, and then modify the calculations that are not suitable for hardware implementation. In hardware, we design a specialized hardware architecture named COPPER based on the memristor. COPPER is capable of efficiently running the modified quantized CSA algorithm and supporting the pipeline further acceleration. The results show that COPPER can perform CSA remarkably well in both speed and energy.

Keywords： Combinatorial optimization     Chaotic simulated annealing     Processing-in-memory    

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Max-margin basedBayesian classifier Article

Tao-cheng HU,Jin-hui YU

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 10,   Pages 973-981 doi: 10.1631/FITEE.1601078

Abstract: There is a tradeoff between generalization capability and computational overhead in multi-class learning. We propose a generative probabilistic multi-class classifier, considering both the generalization capability and the learning/prediction rate. We show that the classifier has a max-margin property. Thus, prediction on future unseen data can nearly achieve the same performance as in the training stage. In addition, local variables are eliminated, which greatly simplifies the optimization problem. By convex and probabilistic analysis, an efficient online learning algorithm is developed. The algorithm aggregates rather than averages dualities, which is different from the classical situations. Empirical results indicate that our method has a good generalization capability and coverage rate.

Keywords： Multi-class learning     Max-margin learning     Online algorithm    

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Looking Back and Prospecting to the Project Design ofthe Spaceflight Launching Site in China

Zhang Zeming

Strategic Study of CAE 2007, Volume 9, Issue 4,   Pages 1-5

Abstract:

This paper introduces the development history of the project design of spaceflight launching site in China and the main forms used in the design, and points out that there shouldn't exist only one fixed mode for the spacecraft launch. China should, according to its national condition, design and build the launching project facility and device that correspond to the technology development of spacecraft and carrier rocket.

Keywords： launching site     project design     development history     integrated mode    

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Design and Implementation of High Performance Security Router BW7000

Xu Mingwei,Xu Ke,Xiong Yongqiang,Jiang Yong,Sun Xiaoxia,Wu Jian,Yu Zhongchao

Strategic Study of CAE 2002, Volume 4, Issue 3,   Pages 54-62

Abstract:

High performance and security are hot areas of the research of Internet. How to provide security protection but not decrease the forwarding performance is a hot research topic currently. This paper is based on the research of the high performance security router, a key project of national high technology research and development plan. Operating system (HEROS) of the high performance router BW7000 was developed independently. In order to provide high performance IP packets forwarding, a high performance routing lookup algorithm based on RAM was developed. A novel classification algorithm based on non-collision Hash-Trie-tree and an algorithm based on distributed packet fair queuing with feedback mechanism weve designed and impemented to support QoS control and security management. In order to secure the network, a router security architecture based on distributed key management was proposed.

Keywords： router     Security     router operating system     route lookup     packet classification     packet scheduling    

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A strategy-proof auction mechanism for service composition based on user preferences

Yao Xia, Zhiqiu Huang,xiayao@nuaa.edu.cn,zqhuang@nuaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 2,   Pages 141-286 doi: 10.1631/FITEE.1900726

Abstract: is an effective method of combining existing atomic services into a value-added service based on cost and quality of service (QoS). To meet the diverse needs of users and to offer pricing services based on QoS, we propose a auction mechanism based on s, which is and can be beneficial in selecting services based on s and dynamically determining the price of services. We have proven that the proposed auction mechanism achieves desirable properties including truthfulness and individual rationality. Furthermore, we propose an auction algorithm to implement the auction mechanism, and carry out extensive experiments based on real data. The results verify that the proposed auction mechanism not only achieves desirable properties, but also helps users find a satisfactory scheme.

Keywords： Combinatorial reverse auction     Service composition     User preference     Strategy-proof     Dynamic pricing    

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A Combined Optimization Model for Transportation Modes of Multimodal Transport

Wang Tao,Wang Gang

Strategic Study of CAE 2005, Volume 7, Issue 10,   Pages 46-50

Abstract:

To solve the prevalent problem of the low informatization level and lack of relevant decision support systems for the multimodal transport in China, transportation characteristics of various transportation modes are analyzed firstly, and choice basis is put forward after comparison. Then a virtual transportation network of multimodal transport is set up. Finally, a combined optimization model for various transportation modes is deduced based on the above and algorithm is also proposed.

Keywords： multimodal transport     transportation mode     combined optimization    

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Studies on Combination Evaluation Methods Based on Nature Differenc

Chen Yantai,Chen Guohong,Li Meijuan

Strategic Study of CAE 2005, Volume 7, Issue 8,   Pages 56-59

Abstract:

This paper firstly defines a group of system evaluation methods of attributes difference. Secondly, it puts forward one new combination evaluation method , in order to solve the problem of inconsistencies in the conclusions, which is caused by adopting many different single methods to evaluate the same objective. This method is used to evaluate the comprehensive competitive power of 15 cities in western China .to make the system evaluation more scientific and useful.

Keywords： evaluation methods with different nature     combination evaluation     re-combination    

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Combinational Evaluation Method Based on Projection Pursuit Algorithm

Wang Shuo, Yang Shanlin, Hu Xiaoxuan

Strategic Study of CAE 2008, Volume 10, Issue 8,   Pages 60-64

Abstract:

The combinational evaluation method which combines TOPSIS with gray correlation degree analysis can improve validity of the evaluation result. In such method, a usual way to assign combinational weight of index is subjective weighting, to assign combinational preference coefficient and identification coefficient is manual weighting. The advantages of combinational evaluation method are not fully mined. Aim at this problem, a method uses projection pursuit algorithm to establish combinational evaluation model is presented. The real coded accelerating genetic algorithm is used to dispose those non-linear problems. The gained index weights are objective. In the same time, a new method of confirms combinational preference coefficients and identification coefficients of gray correlation degree is presented. The example shows the combinational evaluation method based on projection pursuit algorithm is scientific and objective.

Keywords： combinational evaluation     projection pursuit algorithm     real code daccelerating enetic algorithm     identification coefficient     combinational preference coefficient    

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Research on Nonlinear Combination Forecasting Approach Based on BP-AGA

Wang Shuo,Zhang Youfu,Jin Juliang

Strategic Study of CAE 2005, Volume 7, Issue 4,   Pages 83-87

Abstract:

A nonlinear combination forecasting model was established by using neural network and accelerating genetic algorithm (AGA) in the paper. AGA was used to optimize the network parameters as BP approach was slow with training network. Optimization results of AGA were taken as original values of BP approach, the network was trained with BP approach. Network convergence rate was increased with running BP approach and AGA alternately. Meanwhile the part least problem was improved. Examples were presented finally, as a result, the forecasting precision high in evidence.

Keywords： neural network     accelerating genetic algorithm     nonlinear combination forecasting     forecasting precision    

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Semantic composition of distributed representations for query subtopic mining None

Wei SONG, Ying LIU, Li-zhen LIU, Han-shi WANG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 11,   Pages 1409-1419 doi: 10.1631/FITEE.1601476

Abstract:

Inferring query intent is significant in information retrieval tasks. Query subtopic mining aims to find possible subtopics for a given query to represent potential intents. Subtopic mining is challenging due to the nature of short queries. Learning distributed representations or sequences of words has been developed recently and quickly, making great impacts on many fields. It is still not clear whether distributed representations are effective in alleviating the challenges of query subtopic mining. In this paper, we exploit and compare the main semantic composition of distributed representations for query subtopic mining. Specifically, we focus on two types of distributed representations: paragraph vector which represents word sequences with an arbitrary length directly, and word vector composition. We thoroughly investigate the impacts of semantic composition strategies and the types of data for learning distributed representations. Experiments were conducted on a public dataset offered by the National Institute of Informatics Testbeds and Community for Information Access Research. The empirical results show that distributed semantic representations can achieve outstanding performance for query subtopic mining, compared with traditional semantic representations. More insights are reported as well.

Keywords： Subtopic mining     Query intent     Distributed representation     Semantic composition    

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Title Author Date Type Operation

Web page classification based on heterogeneous features and a combination of multiple classifiers

Li Deng, Xin Du, Ji-zhong Shen,jzshen@zju.edu.cn

Journal Article

A Rough Fuzzy Neural Classifier

Zeng Huanglin,Wang Xiao

Journal Article

Representation learning via a semi-supervised stacked distance autoencoder for image classification

Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang,jliang@zju.edu.cn

Journal Article

A new constrained maximum margin approach to discriminative learning of Bayesian classifiers

Ke GUO, Xia-bi LIU, Lun-hao GUO, Zong-jie LI, Zeng-min GENG

Journal Article

Joint tracking and classification of extended targets with complex shapes

Liping Wang, Ronghui Zhan, Yuan Huang, Jun Zhang, Zhaowen Zhuang,zhanrh@nudt.edu.cn

Journal Article

NEHASH: high-concurrency extendible hashing for non-volatile memory

Qiankun WANG, Xingchen LI, Bingzhe WU, Ke YANG, Wei HU, Guangyu SUN, Yuchao YANG,gsun@pku.edu.cn

Journal Article

Max-margin basedBayesian classifier

Tao-cheng HU,Jin-hui YU

Journal Article

Looking Back and Prospecting to the Project Design ofthe Spaceflight Launching Site in China

Zhang Zeming

Journal Article

Design and Implementation of High Performance Security Router BW7000

Xu Mingwei,Xu Ke,Xiong Yongqiang,Jiang Yong,Sun Xiaoxia,Wu Jian,Yu Zhongchao

Journal Article

A strategy-proof auction mechanism for service composition based on user preferences

Yao Xia, Zhiqiu Huang,xiayao@nuaa.edu.cn,zqhuang@nuaa.edu.cn

Journal Article

A Combined Optimization Model for Transportation Modes of Multimodal Transport

Wang Tao,Wang Gang

Journal Article

Studies on Combination Evaluation Methods Based on Nature Differenc

Chen Yantai,Chen Guohong,Li Meijuan

Journal Article

Combinational Evaluation Method Based on Projection Pursuit Algorithm

Wang Shuo, Yang Shanlin, Hu Xiaoxuan

Journal Article

Research on Nonlinear Combination Forecasting Approach Based on BP-AGA

Wang Shuo,Zhang Youfu,Jin Juliang

Journal Article

Semantic composition of distributed representations for query subtopic mining

Wei SONG, Ying LIU, Li-zhen LIU, Han-shi WANG

Journal Article