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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 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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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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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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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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Automatic malware classification and new malwaredetection using machine learning Article

Liu LIU, Bao-sheng WANG, Bo YU, Qiu-xi ZHONG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 9,   Pages 1336-1347 doi: 10.1631/FITEE.1601325

Abstract: The explosive growth of malware variants poses a major threatto information security. Traditional anti-virus systems based on signaturesfail to classify unknown malware into their corresponding familiesand to detect new kinds of malware programs. Therefore, we proposea machine learning based malware analysis system, which is composedof three modules: data processing, decision making, and new malwaredetection. The data processing module deals with gray-scale images,Opcode n-gram, and import functions, which are employed to extractthe features of the malware. The decision-making module uses the featuresto classify the malware and to identify suspicious malware. Finally,the detection module uses the shared nearest neighbor (SNN) clusteringalgorithm to discover new malware families. Our approach is evaluatedon more than 20 000 malware instances, which were collected by Kingsoft,ESET NOD32, and Anubis. The results show that our system can effectivelyclassify the unknown malware with a best accuracy of 98.9%, and successfullydetects 86.7% of the new malware.

Keywords： Malware classification     Machine learning     n-gram     Gray-scale image     Feature extraction     Malware detection    

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Feature selection techniques for microarray datasets: a comprehensive review, taxonomy, and future directions Review

Kulanthaivel BALAKRISHNAN, Ramasamy DHANALAKSHMI,bala.k.btech@gmail.com,r_dhanalakshmi@yahoo.com

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 10,   Pages 1451-1478 doi: 10.1631/FITEE.2100569

Abstract:

For optimal results, retrieving a relevant feature from a has become a hot topic for researchers involved in the study of (FS) techniques. The aim of this review is to provide a thorough description of various, recent FS techniques. This review also focuses on the techniques proposed for s to work on multiclass classification problems and on different ways to enhance the performance of learning algorithms. We attempt to understand and resolve the imbalance problem of datasets to substantiate the work of researchers working on s. An analysis of the literature paves the way for comprehending and highlighting the multitude of challenges and issues in finding the optimal feature subset using various FS techniques. A case study is provided to demonstrate the process of implementation, in which three microarray cancer datasets are used to evaluate the classification accuracy and convergence ability of several wrappers and hybrid algorithms to identify the optimal feature subset.

Keywords： Feature selection     High dimensionality     Learning techniques     Microarray dataset    

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Classification of EEG-based single-trial motor imagery tasks using aB-CSP method forBCI Research Articles

Zhi-chuan TANG, Chao LI, Jian-feng WU, Peng-cheng LIU, Shi-wei CHENG

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 8,   Pages 1087-1098 doi: 10.1631/FITEE.1800083

Abstract: Classifying single-trial electroencephalogram (EEG) based motor imagery (MI) tasks is extensively used to control brain-computer interface (BCI) applications, as a communication bridge between humans and computers. However, the low signal-to-noise ratio and individual differences of EEG can affect the classification results negatively. In this paper, we propose an improved common spatial pattern (B-CSP) method to extract features for alleviating these adverse effects. First, for different subjects, the method of Bhattacharyya distance is used to select the optimal frequency band of each electrode including strong event-related desynchronization (ERD) and event-related synchronization (ERS) patterns; then the signals of the optimal frequency band are decomposed into spatial patterns, and the features that can describe the maximum differences of two classes of MI are extracted from the EEG data. The proposed method is applied to the public data set and experimental data set to extract features which are input into a back propagation neural network (BPNN) classifier to classify single-trial MI EEG. Another two conventional feature extraction methods, original common spatial pattern (CSP) and autoregressive (AR), are used for comparison. An improved classification performance for both data sets (public data set: 91.25%±1.77% for left hand vs. foot and 84.50%±5.42% for left hand vs. right hand; experimental data set: 90.43%±4.26% for left hand vs. foot) verifies the advantages of the B-CSP method over conventional methods. The results demonstrate that our proposed B-CSP method can classify EEG-based MI tasks effectively, and this study provides practical and theoretical approaches to BCI applications.

Keywords： Electroencephalogram (EEG)     Motor imagery (MI)     Improved common spatial pattern (B-CSP)     Feature extraction     Classification    

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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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Astatistical distribution texton feature for synthetic aperture radar image classification Article

Chu HE, Ya-ping YE, Ling TIAN, Guo-peng YANG, Dong CHEN

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 10,   Pages 1614-1623 doi: 10.1631/FITEE.1601051

Abstract: We propose a novel statistical distribution texton (s-texton) feature for synthetic aperture radar (SAR) image classification. Motivated by the traditional texton feature, the framework of texture analysis, and the importance of statistical distribution in SAR images, the s-texton feature is developed based on the idea that parameter estimation of the statistical distribution can replace the filtering operation in the traditional texture analysis of SAR images. In the process of extracting the s-texton feature, several strategies are adopted, including pre-processing, spatial gridding, parameter estimation, texton clustering, and histogram

Keywords： Synthetic aperture radar     Statistical distribution     Parameter estimation     Image 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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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 new feature selection method for handling redundant information in text classification

You-wei WANG, Li-zhou FENG

Journal Article

A Rough Fuzzy Neural Classifier

Zeng Huanglin,Wang Xiao

Journal Article

Two-level hierarchical feature learning for image classification

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

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

Automatic malware classification and new malwaredetection using machine learning

Liu LIU, Bao-sheng WANG, Bo YU, Qiu-xi ZHONG

Journal Article

Feature selection techniques for microarray datasets: a comprehensive review, taxonomy, and future directions

Kulanthaivel BALAKRISHNAN, Ramasamy DHANALAKSHMI,bala.k.btech@gmail.com,r_dhanalakshmi@yahoo.com

Journal Article

Classification of EEG-based single-trial motor imagery tasks using aB-CSP method forBCI

Zhi-chuan TANG, Chao LI, Jian-feng WU, Peng-cheng LIU, Shi-wei CHENG

Journal Article

Max-margin basedBayesian classifier

Tao-cheng HU,Jin-hui YU

Journal Article

Astatistical distribution texton feature for synthetic aperture radar image classification

Chu HE, Ya-ping YE, Ling TIAN, Guo-peng YANG, Dong CHEN

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