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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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Words alignment based on association rules for cross-domain sentiment classification None

Xi-bin JIA, Ya JIN, Ning LI, Xing SU, Barry CARDIFF, Bir BHANU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 2,   Pages 260-272 doi: 10.1631/FITEE.1601679

Abstract: Automatic classification of sentiment data (e.g., reviews, blogs) has many applications in enterprise user management systems, and can help us understand people’s attitudes about products or services. However, it is difficult to train an accurate sentiment classifier for different domains. One of the major reasons is that people often use different words to express the same sentiment in different domains, and we cannot easily find a direct mapping relationship between them to reduce the differences between domains. So, the accuracy of the sentiment classifier will decline sharply when we apply a classifier trained in one domain to other domains. In this paper, we propose a novel approach called words alignment based on association rules (WAAR) for cross-domain sentiment classification, which can establish an indirect mapping relationship between domain-specific words in different domains by learning the strong association rules between domain-shared words and domain-specific words in the same domain. In this way, the differences between the source domain and target domain can be reduced to some extent, and a more accurate cross-domain classifier can be trained. Experimental results on AmazonR datasets show the effectiveness of our approach on improving the performance of cross-domain sentiment classification.

Keywords： Sentiment classification     Cross-domain     Association rules    

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One-Variable Attack on The Industrial Fault Classification System and Its Defense Article

Yue Zhuo, Yuri A.W. Shardt, Zhiqiang Ge

Engineering 2022, Volume 19, Issue 12,   Pages 240-251 doi: 10.1016/j.eng.2021.07.033

Abstract:

Recently developed fault classification methods for industrial processes are mainly data-driven. Notably, models based on deep neural networks have significantly improved fault classification accuracy owing to the inclusion of a large number of data patterns. However, these data-driven models are vulnerable to adversarial attacks; thus, small perturbations on the samples can cause the models to provide incorrect fault predictions. Several recent studies have demonstrated the vulnerability of machine learning methods and the existence of adversarial samples. This paper proposes a black-box attack method with an extreme constraint for a safe-critical industrial fault classification system: Only one variable can be perturbed to craft adversarial samples. Moreover, to hide the adversarial samples in the visualization space, a Jacobian matrix is used to guide the perturbed variable selection, making the adversarial samples in the dimensional reduction space invisible to the human eye. Using the one-variable attack (OVA) method, we explore the vulnerability of industrial variables and fault types, which can help understand the geometric characteristics of fault classification systems. Based on the attack method, a corresponding adversarial training defense method is also proposed, which efficiently defends against an OVA and improves the prediction accuracy of the classifiers. In experiments, the proposed method was tested on two datasets from the Tennessee–Eastman process (TEP) and Steel Plates (SP). We explore the vulnerability and correlation within variables and faults and verify the effectiveness of OVAs and defenses for various classifiers and datasets. For industrial fault classification systems, the attack success rate of our method is close to (on TEP) or even higher than (on SP) the current most effective first-order white-box attack method, which requires perturbation of all variables.

Keywords： Adversarial samples     Black-box attack     Industrial data security     Fault classification system    

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Approximation Space of Generalization and Rough Classification Algebra

Liu Yonghong

Strategic Study of CAE 2006, Volume 8, Issue 3,   Pages 39-48

Abstract:

Some new basic concepts are proposed in this paper, including the approximation space of generalization, rough approximation axiom, disturbance set axiom, classification principle of rough set, principle of nondeterministic even set, principle of concentration, game classification, quantum logic classification, bit quantum symmetric classification, incomparable set, bit space set, protocol relation, rough set function of protocol relation, rough classification algebra, rough simple algebra, etc. , and a guess is also proposed. The mathematics viewpoints of knowledge are shown. Based on the protocol relation, the rough quotient algebra and the rough subalgebra are constructed. The avoidance-merge algorithm (A - M algorithm) is presented and an example is given.

Keywords： approximation space of generalization     rough set     rough classification algebra     protocol relation     rough simple algebra     rough quotient algebra     rough subalgebra    

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Supporting Basic Research by Classification to Promote Whole-Chain Disruptive Technology Innovation

Zhang Huiqin, Ping Jing, Sun Changpu

Strategic Study of CAE 2018, Volume 20, Issue 6,   Pages 24-26 doi: 10.15302/J-SSCAE-2018.06.004

Abstract:

In recent years, China has made great progress in science and technology, but still falls behind in terms of key technologies. To promote original innovation in science and technology and fundamentally overcome its weakness in key technologies, China should focus on basic research. To explore how basic research creates disruptive technologies, this paper expounds the connotation of basic research, and categorizes basic research into four types according to its targets: basic research toward major scientific objectives, basic research driven by national demands, talent-based basic research for free exploration, and basic research aimed at practical application. Based on the analysis of the characteristics and development needs of various types of basic research, we put forward a development suggestion of providing differentiated and stable support to promote whole-chain disruptive technology innovation.

Keywords： basic research     disruptive technology     differentiated support    

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A Framework Design of the Future Engineer Classification of China

Wen Liang,Weng Jingbo,He Jishan

Strategic Study of CAE 2007, Volume 9, Issue 8,   Pages 15-20

Abstract:

The UK pattern and the USA pattern have been acknowledged internationally as the typical certified engineer patterns.  A few developed countries have already established the mutual acceptance agreement on engineer qualification on the basis of the engineer system.  The consistency of engineers'educational background,  working experience,  qualification examination and further education are regarded as the basis of these international engineer agreements.  The Personnel Ministry of China has carried out various engineer management systems in different stages,  which are beneficial to the economic growth of the country, and the Personnel Ministry has also done years of fruitful explorations and obtained rich experience in the trial implementation of qualification logon system.  The authors think that the main principle of China's new engineer specialty classification is to accord with its national conditions and be in line with the international conventions.  China's engineer specialty classification should be fused with the international engineer specialty classification,  consistent with the basic principle of the international engineer mutual identification and its own present engineer specialty classification situation.  According to whether the specialty relates to the social and public benefit and the citizen security of life and property,  the new engineer specialty can be classified into two categories: registered engineers and non-registered engineers.  Drawing on the experience of UK and USA engineer specialty classifications, synthesizing China's present situation,  and summarizing a series of the current occupation classifications and the code table of discipline classification,  the Chinese engineers can be classified into thirty-five categories according to specialties. Certain categories can be further divided into several sub-categories. Suggestions about constructing EMC are provided

Keywords： China     classification of engineers’ occupation     certified engineer system     mutual acceptance agreement of international engineers    

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A Study on the Classification and Resource Utilization of Rural Waste in China

Huhetaoli,Yuan Haoran,Liu Xiaofeng,Chen Hanping,Lei Tingzhou and Chen Yong

Strategic Study of CAE 2017, Volume 19, Issue 4,   Pages 103-108 doi: 10.15302/J-SSCAE-2017.04.016

Abstract:

This paper examines the problems inherent in the classification and utilization of rural waste in China. It starts by expounding the generation, hazards, and recycling significance of rural waste. Next, it deeply analyzes the issues that challenge the resource utilization of rural waste by evaluating the generation quantity, regional distribution characteristics, and resource-utilization situation of rural waste in China. A reasonable design for a development path and phased targets is then provided, based on the current goal of beautiful countryside construction and on the development trend of the resource utilization of rural waste. Finally supporting measures and policy suggestions are proposed for future resource development and utilization of rural waste.

Keywords： rural waste     classification     resource     development strategy     policy suggestion    

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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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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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TIE algorithm: a layer over clustering-based taxonomy generation for handling evolving data None

Rabia IRFAN, Sharifullah KHAN, Kashif RAJPOOT, Ali Mustafa QAMAR

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 6,   Pages 763-782 doi: 10.1631/FITEE.1700517

Abstract: Taxonomy is generated to effectively organize and access large volume of data. A taxonomy is a way of representing concepts that exist in data. It needs to continuously evolve to reflect changes in data. Existing automatic taxonomy generation techniques do not handle the evolution of data; therefore, the generated taxonomies do not truly represent the data. The evolution of data can be handled by either regenerating taxonomy from scratch, or allowing taxonomy to incrementally evolve whenever changes occur in the data. The former approach is not economical in terms of time and resources. A taxonomy incremental evolution (TIE) algorithm, as proposed, is a novel attempt to handle the data that evolve in time. It serves as a layer over an existing clustering-based taxonomy generation technique and allows an existing taxonomy to incrementally evolve. The algorithm was evaluated in research articles selected from the computing domain. It was found that the taxonomy using the algorithm that evolved with data needed considerably shorter time, and had better quality per unit time as compared to the taxonomy regenerated from scratch.

Keywords： Taxonomy     Clustering algorithms     Information science     Knowledge management     Machine learning    

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Shot classification and replay detection for sports video summarization Research Article

Ali JAVED, Amen ALI KHAN,ali.javed@uettaxila.edu.pk

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 5,   Pages 790-800 doi: 10.1631/FITEE.2000414

Abstract: Automated analysis of sports is challenging due to variations in cameras, replay speed, illumination conditions, editing effects, game structure, genre, etc. To address these challenges, we propose an effective framework based on and for field sports videos. Accurate is mandatory to better structure the input video for further processing, i.e., key events or . Therefore, we present a based method for . Then we analyze each shot for and specifically detect the successive batch of logo transition frames that identify the replay segments from the sports videos. For this purpose, we propose local octa-pattern features to represent video frames and train the for classification as replay or non-replay frames. The proposed framework is robust to variations in cameras, replay speed, shot speed, illumination conditions, game structure, sports genre, broadcasters, logo designs and placement, frame transitions, and editing effects. The performance of our framework is evaluated on a dataset containing diverse YouTube sports videos of soccer, baseball, and cricket. Experimental results demonstrate that the proposed framework can reliably be used for and to summarize field sports videos.

Keywords： Extreme learning machine     Lightweight convolutional neural network     Local octa-patterns     Shot classification     Replay detection     Video summarization    

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A new feature selection method for handling redundant information in text classification None

You-wei WANG, Li-zhou FENG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 2,   Pages 221-234 doi: 10.1631/FITEE.1601761

Abstract: Feature selection is an important approach to dimensionality reduction in the field of text classification. Because of the difficulty in handling the problem that the selected features always contain redundant information, we propose a new simple feature selection method, which can effectively filter the redundant features. First, to calculate the relationship between two words, the definitions of word frequency based relevance and correlative redundancy are introduced. Furthermore, an optimal feature selection (OFS) method is chosen to obtain a feature subset FS1. Finally, to improve the execution speed, the redundant features in FS1 are filtered by combining a predetermined threshold, and the filtered features are memorized in the linked lists. Experiments are carried out on three datasets (WebKB, 20-Newsgroups, and Reuters-21578) where in support vector machines and naïve Bayes are used. The results show that the classification accuracy of the proposed method is generally higher than that of typical tradi-tional methods (information gain, improved Gini index, and improved comprehensively measured feature selection) and the OFS methods. Moreover, the proposed method runs faster than typical mutual information-based methods (improved and normalized mutual information-based feature selections, and multilabel feature selection based on maximum dependency and minimum redundancy) while simultaneously ensuring classification accuracy. Statistical results validate the effectiveness of the proposed method in handling redundant information in text classification.

Keywords： Feature selection     Dimensionality reduction     Text classification     Redundant features     Support vector machine     Naïve Bayes     Mutual information    

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Two-level hierarchical feature learning for image classification Article

Guang-hui SONG,Xiao-gang JIN,Gen-lang CHEN,Yan NIE

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 9,   Pages 897-906 doi: 10.1631/FITEE.1500346

Abstract: In some image classification tasks, similarities among different categories are different and the samples are usually misclassified as highly similar categories. To distinguish highly similar categories, more specific features are required so that the classifier can improve the classification performance. In this paper, we propose a novel two-level hierarchical feature learning framework based on the deep convolutional neural network (CNN), which is simple and effective. First, the deep feature extractors of different levels are trained using the transfer learning method that fine-tunes the pre-trained deep CNN model toward the new target dataset. Second, the general feature extracted from all the categories and the specific feature extracted from highly similar categories are fused into a feature vector. Then the final feature representation is fed into a linear classifier. Finally, experiments using the Caltech-256, Oxford Flower-102, and Tasmania Coral Point Count (CPC) datasets demonstrate that the expression ability of the deep features resulting from two-level hierarchical feature learning is powerful. Our proposed method effectively increases the classification accuracy in comparison with flat multiple classification methods.

Keywords： Transfer learning     Feature learning     Deep convolutional neural network     Hierarchical classification     Spectral clustering    

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Classification Model and Its Application of Stability of Roadway Driving Along Next Goaf for Fully-mechanized Caving Face

Zhu Chuanqu,Wang Weijun,Shi Shiliang

Strategic Study of CAE 2006, Volume 8, Issue 3,   Pages 35-38

Abstract:

The stability of roadway driving along next goaf for fully-mechanized caving face is synthetically influenced by many factors such as intensity of surrounding rock, intensity of coal, mining depth, joint and crack of surrounding rock, mining operation, top coal thickness, width of pillar and roadway section. On the basis of theoretical analysis, practical experience and observation data, the subordination functions of the factors influencing the stability of roadway driving along next goaf for fully-mechanized caving face are structured and the grey-fuzzy classification model is established. The application of examples shows that the model is accurate and reliable, and plays an important part in the support design, construction and management of roadway driving along next goaf for fully-mechanized caving face.

Keywords： roadway driving along next goaf for fully-mechanized caving face     stability of surrounding rock     classification model     subordination function    

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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

Words alignment based on association rules for cross-domain sentiment classification

Xi-bin JIA, Ya JIN, Ning LI, Xing SU, Barry CARDIFF, Bir BHANU

Journal Article

One-Variable Attack on The Industrial Fault Classification System and Its Defense

Yue Zhuo, Yuri A.W. Shardt, Zhiqiang Ge

Journal Article

Approximation Space of Generalization and Rough Classification Algebra

Liu Yonghong

Journal Article

Supporting Basic Research by Classification to Promote Whole-Chain Disruptive Technology Innovation

Zhang Huiqin, Ping Jing, Sun Changpu

Journal Article

A Framework Design of the Future Engineer Classification of China

Wen Liang,Weng Jingbo,He Jishan

Journal Article

A Study on the Classification and Resource Utilization of Rural Waste in China

Huhetaoli,Yuan Haoran,Liu Xiaofeng,Chen Hanping,Lei Tingzhou and Chen Yong

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

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

TIE algorithm: a layer over clustering-based taxonomy generation for handling evolving data

Rabia IRFAN, Sharifullah KHAN, Kashif RAJPOOT, Ali Mustafa QAMAR

Journal Article

Shot classification and replay detection for sports video summarization

Ali JAVED, Amen ALI KHAN,ali.javed@uettaxila.edu.pk

Journal Article

A new feature selection method for handling redundant information in text classification

You-wei WANG, Li-zhou FENG

Journal Article

Two-level hierarchical feature learning for image classification

Guang-hui SONG,Xiao-gang JIN,Gen-lang CHEN,Yan NIE

Journal Article

Classification Model and Its Application of Stability of Roadway Driving Along Next Goaf for Fully-mechanized Caving Face

Zhu Chuanqu,Wang Weijun,Shi Shiliang

Journal Article