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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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Information schema constructs for instantiationand composition of system manifestation features Article

Shahab POURTALEBI, Imre HORVÁTH

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 9,   Pages 1396-1415 doi: 10.1631/FITEE.1601235

Abstract: Complementing our previous publications, this paper presentsthe information schema constructs (ISCs) that underpin the programmingof specific system manifestation feature (SMF) orientated informationmanagement and composing system models. First, we briefly present(1) the general process of pre-embodiment design with SMFs, (2) theprocedures of creating genotypes and phenotypes of SMFs, (3) the specificprocedure of instantiation of phenotypes of SMFs, and (4) the procedureof system model management and processing. Then, the chunks of informationneeded for instantiation of phenotypes of SMFs are discussed, andthe ISCs designed for instantiation presented. Afterwards, the informationmanagement aspects of system modeling are addressed. Methodologically,system modeling involves (1) placement of phenotypes of SMF in themodeling space, (2) combining them towards the desired architectureand operation, (3) assigning values to the parameters and checkingthe satisfaction of constraints, and (4) storing the system modelin the SMFs-based warehouse database. The final objective of the reportedresearch is to develop an SMFs-based toolbox to support modeling ofcyber-physical systems (CPSs).

Keywords： System manifestation features (SMFs)     Information schema constructs     Database schemata     SMF genotypes     SMF phenotypes     SMF instances     Software tool box     System-level design     Cyber-physical systems    

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Algorithm Design for Improving Feature Extraction Efficiency Based on KPCA

Xu Yong,Yangjingyu,Lu Jianfeng

Strategic Study of CAE 2005, Volume 7, Issue 10,   Pages 38-42

Abstract:

KPCA (kernel PCA) is derived from PCA. It can extract nonlinear feature components of samples. However, feature extraction for one sample requires that kernel functions between training samples and the sample be calculated in advance. So, the size of training sample set affects the efficiency of feature extraction. It is supposed that in feature space the eigenvectors may be linearly expressed by a part of training samples, called nodes. According to the supposition, an improved KPCA (IKPCA) algorithm is developed. IKPCA extracts feature components of one sample efficiently, only based on kernel functions between nodes and the sample. Experimental results show that IKPCA is very close to KPCA in performance, while with higher efficiency.

Keywords： KPCA(Kernel PCA)     IKPCA(Improved KPCA)     feature extraction     feature space    

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Information schema constructs for defining warehouse databases of genotypes and phenotypes of system manifestation features Article

Shahab POURTALEBI,Imre HORVÁTH

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 9,   Pages 862-884 doi: 10.1631/FITEE.1600997

Abstract: Our long-term objective is to develop a software toolbox for pre-embodiment design of complex and heterogeneous systems, such as cyber-physical systems. The novelty of this toolbox is that it uses system manifestation features (SMFs) for transdisciplinary modeling of these systems. The main challenges of implementation of the toolbox are functional design- and language-independent computational realization of the warehouses, and systematic development and management of the various evolving implements of SMFs (genotypes, phenotypes, and instances). Therefore, an information schema construct (ISC) based approach is proposed to create the schemata of the associated warehouse databases and the above-mentioned SMF implements. ISCs logically arrange the data contents of SMFs in a set of relational tables of varying semantics. In this article we present the ISCs necessary for creation of genotypes and phenotypes. They increase the efficiency of the database development process and make the data relationships transparent. Our follow-up research focuses on the elaboration of the SMF instances based system modeling methodology.

Keywords： Cyber-physical systems     Software toolbox     Pre-embodiment design     System manifestation features (SMFs)     Warehouses     Database schemata     SMF genotypes     SMF phenotypes     SMF instances     Information schema constructs    

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Afeature selection approach based on a similarity measure for software defect prediction Article

Qiao YU, Shu-juan JIANG, Rong-cun WANG, Hong-yang WANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1744-1753 doi: 10.1631/FITEE.1601322

Abstract: Software defect prediction is aimed to find potential defects based on historical data and software features. Software features can reflect the characteristics of software modules. However, some of these features may be more relevant to the class (defective or non-defective), but others may be redundant or irrelevant. To fully measure the correlation between different features and the class, we present a feature selection approach based on a similarity measure (SM) for software defect prediction. First, the feature weights are updated according to the similarity of samples in different classes. Second, a feature ranking list is generated by sorting the feature weights in descending order, and all feature subsets are selected from the feature ranking list in sequence. Finally, all feature subsets are evaluated on a k-nearest neighbor (KNN) model and measured by an area under curve (AUC) metric for classification performance. The experiments are conducted on 11 National Aeronautics and Space Administration (NASA) datasets, and the results show that our approach performs better than or is comparable to the compared feature selection approaches in terms of classification performance.

Keywords： Software defect prediction     Feature selection     Similarity measure     Feature weights     Feature ranking list    

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A Face Recognition Based on Fusion Features Extraction From Two Kinds of Projection

Zhang Shengliang,Xu Yong,Yang Jian,Yang Jingyu

Strategic Study of CAE 2006, Volume 8, Issue 8,   Pages 50-55

Abstract:

A novel face recognition algorithm based on two kinds of projection is presented in this paper. First, the two dimension principal component analysis (2DPCA) is used to extract one group of features, denoted by α. Second, the fisher linear discriminant analysis (LDA) , or fisherfaces, is used for extracting another group of features, denoted by β.After being standardized, the two kinds of features are combined together in the form of the complex vector α+iβ. Then the fusion features in the complex feature space is extracted by using complex PCA (CPCA). The proposed algorithm is evaluated by using the FERET face database at three different resolutions. The experimental results indicate that the proposed method can achieve about 10% higher recognition accurate rate than 2DPCA and LDA, while only using 28 features for each sample.

Keywords： feature fusion     linear discriminant analysis (LDA)     feature extraction     face recognition    

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The Karst development characteristics and countermeasure of Yunwushan Tunnel

Xue Bin, Zhang Minqing

Strategic Study of CAE 2009, Volume 11, Issue 12,   Pages 61-68

Abstract:

The revealed karst, karst features and treatment measures are described in detail, and different types of karst-specific countermeasures are put up. At the same time, it is stressed that in the encounter of large-scale high-pressure water-rich cavity filling, macro-scopical analysis and understanding of the geological background governs in determining the treatment schedule. The by-pass schedule pertaining to large-scale karst cavities during construction is put up, and relatively construction organization is modified to ensure the project term target. All these can provide reference for the construction of the long karst tunnels.

Keywords： Yunwushan Tunnel     Karst     development characteristics     countermeasure    

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Research on Features of Energy Utilized in the Field of Consumption

Jiang Yi,Zhu Andong and Guo Siyue

Strategic Study of CAE 2015, Volume 17, Issue 8,   Pages 122-131

Abstract:

According to the features of using process, the energy use could be divided into two categories including the energy use in the field of consumption (EUFC) to provide services and the energy using in the field of production (EUFP) to provide products. The feature of EUFC is different with EUFP, including evaluation methods, energy saving approach and developing strategy, etc. Considering its potential to be the main growing sector in the next period of energy consumption, more attention should be paid to EUFC. In this paper, based on the analysis of its features, the measure of EUFC and the energy saving approach are recommended, as well as the suggestions to the present situation in China.

Keywords： the field of consumption     the field of production     energy consumption     feature    

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Spontaneous versus posed smile recognition via region-specific texture descriptor and geometric facial dynamics Article

Ping-ping WU, Hong LIU, Xue-wu ZHANG, Yuan GAO

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 7,   Pages 955-967 doi: 10.1631/FITEE.1600041

Abstract: As a typical biometric cue with great diversities, smile is a fairly influential signal in social interaction, which reveals the emotional feeling and inner state of a person. Spontaneous and posed smiles initiated by different brain systems have differences in both morphology and dynamics. Distinguishing the two types of smiles remains challenging as discriminative subtle changes need to be captured, which are also uneasily observed by human eyes. Most previous related works about spontaneous versus posed smile recognition concentrate on extracting geometric features while appearance features are not fully used, leading to the loss of texture information. In this paper, we propose a region-specific texture descriptor to represent local pattern changes of different facial regions and compensate for limitations of geometric features. The temporal phase of each facial region is divided by calculating the intensity of the corresponding facial region rather than the intensity of only the mouth region. A mid-level fusion strategy of support vector machine is employed to combine the two feature types. Experimental results show that both our proposed appearance representation and its combination with geometry-based facial dynamics achieve favorable performances on four baseline databases: BBC, SPOS, MMI, and UvA-NEMO.

Keywords： Facial landmark localization     Geometric feature     Appearance feature     Smile recognition    

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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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Featurematching using quasi-conformalmaps Article

Chun-xue WANG, Li-gang LIU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 5,   Pages 644-657 doi: 10.1631/FITEE.1500411

Abstract: We present a fully automatic method for finding geometrically consistent correspondences while discarding outliers from the candidate point matches in two images. Given a set of candidate matches provided by scale-invariant feature transform (SIFT) descriptors, which may contain many outliers, our goal is to select a subset of these matches retaining much more geometric information constructed by a mapping searched in the space of all diffeomorphisms. This problem can be formulated as a constrained optimization involving both the Beltrami coefficient (BC) term and quasi-conformal map, and solved by an efficient iterative algorithm based on the variable splitting method. In each iteration, we solve two subproblems, namely a linear system and linearly constrained convex quadratic programming. Our algorithm is simple and robust to outliers. We show that our algorithm enables producing more correct correspondences experimentally compared with state-of-the-art approaches.

Keywords： Feature correspondence     Quasi-conformal map     Splitting method    

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Improving entity linking with two adaptive features Research Article

Hongbin ZHANG, Quan CHEN, Weiwen ZHANG

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 11,   Pages 1620-1630 doi: 10.1631/FITEE.2100495

Abstract:

(EL) is a fundamental task in natural language processing. Based on neural networks, existing systems pay more attention to the construction of the , but ignore latent semantic information in the and the acquisition of effective information. In this paper, we propose two , in which the first adaptive feature enables the local and s to capture latent information, and the second adaptive feature describes effective information for embeddings. These can work together naturally to handle some uncertain information for EL. Experimental results demonstrate that our EL system achieves the best performance on the AIDA-B and MSNBC datasets, and the best average performance on out-domain datasets. These results indicate that the proposed , which are based on their own diverse contexts, can capture information that is conducive for EL.

Keywords： Entity linking     Local model     Global model     Adaptive features     Entity type    

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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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Analysis registration group features on “two-child fertility policy” in Guangzhou

Tang Yunge,Li Feicheng,Han Liwei

Strategic Study of CAE 2015, Volume 17, Issue 6,   Pages 82-85

Abstract:

In this paper we take the “two-child fertility policy” registration group as an example and analyse their demographical features,predict the effects of fertility policy adjustment to social and individuals, promote the continuous improvement of fertility policy, having very important realistic significance.

Keywords： two-child fertility policy     group features     Guangdong Provice    

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Fast uniform content-based satellite image registration using the scale-invariant feature transform descriptor Article

Hamed BOZORGI, Ali JAFARI

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 8,   Pages 1108-1116 doi: 10.1631/FITEE.1500295

Abstract: Content-based satellite image registration is a difficult issue in the fields of remote sensing and image processing. The difficulty is more significant in the case of matching multisource remote sensing images which suffer from illumination, rotation, and source differences. The scale-invariant feature transform (SIFT) algorithm has been used successfully in satellite image registration problems. Also, many researchers have applied a local SIFT descriptor to improve the image retrieval process. Despite its robustness, this algorithm has some difficulties with the quality and quantity of the extracted local feature points in multisource remote sensing. Furthermore, high dimensionality of the local features extracted by SIFT results in time-consuming computational processes alongside high storage requirements for saving the relevant information, which are important factors in content-based image retrieval (CBIR) applications. In this paper, a novel method is introduced to transform the local SIFT features to global features for multisource remote sensing. The quality and quantity of SIFT local features have been enhanced by applying contrast equalization on images in a pre-processing stage. Considering the local features of each image in the reference database as a separate class, linear discriminant analysis (LDA) is used to transform the local features to global features while reducing dimensionality of the feature space. This will also significantly reduce the computational time and storage required. Applying the trained kernel on verification data and mapping them showed a successful retrieval rate of 91.67% for test feature points.

Keywords： Content-based image retrieval     Feature point distribution     Image registration     Linear discriminant analysis     Remote sensing     Scale-invariant feature transform    

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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

Information schema constructs for instantiationand composition of system manifestation features

Shahab POURTALEBI, Imre HORVÁTH

Journal Article

Algorithm Design for Improving Feature Extraction Efficiency Based on KPCA

Xu Yong,Yangjingyu,Lu Jianfeng

Journal Article

Information schema constructs for defining warehouse databases of genotypes and phenotypes of system manifestation features

Shahab POURTALEBI,Imre HORVÁTH

Journal Article

Afeature selection approach based on a similarity measure for software defect prediction

Qiao YU, Shu-juan JIANG, Rong-cun WANG, Hong-yang WANG

Journal Article

A Face Recognition Based on Fusion Features Extraction From Two Kinds of Projection

Zhang Shengliang,Xu Yong,Yang Jian,Yang Jingyu

Journal Article

The Karst development characteristics and countermeasure of Yunwushan Tunnel

Xue Bin, Zhang Minqing

Journal Article

Research on Features of Energy Utilized in the Field of Consumption

Jiang Yi,Zhu Andong and Guo Siyue

Journal Article

Spontaneous versus posed smile recognition via region-specific texture descriptor and geometric facial dynamics

Ping-ping WU, Hong LIU, Xue-wu ZHANG, Yuan GAO

Journal Article

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

You-wei WANG, Li-zhou FENG

Journal Article

Featurematching using quasi-conformalmaps

Chun-xue WANG, Li-gang LIU

Journal Article

Improving entity linking with two adaptive features

Hongbin ZHANG, Quan CHEN, Weiwen ZHANG

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

Analysis registration group features on “two-child fertility policy” in Guangzhou

Tang Yunge,Li Feicheng,Han Liwei

Journal Article

Fast uniform content-based satellite image registration using the scale-invariant feature transform descriptor

Hamed BOZORGI, Ali JAFARI

Journal Article