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2010 Chinese Control and Decision Conference

作者： Ruhai Lei,Xiaoxiao Kong,Xuesong Wang School of Information and Electrical Engineering,China University of Mining and Technology,Xuzhou,Jiangsu 221116 China

＜正＞In order to improve the generalization performance of support vector machine（SVM）,a kind of ensemble SVM using an entropy-based attribute selection method was *** entropy metric based on similarity between objects was designed to evaluate the importance degree of each attribute and so as to obtain a set of important attributes. Based on the set of important attributes,the Bagging method was used to generate sub-SVMs and then the majority voting rule was adopted to obtain the final ensemble result of all *** proposed ensemble method can avoid destructing the attribute relativity or attribute dependence by selecting an attribute subset from original attribute space *** performance of single SVM can be improved and the diversity between sub-SVMs can also be *** results on UCI testing datasets show that the proposed ensemble method can improve the classification precision of SVM and make the ensemble SVM has better generalization property.

关键词： support vector machine ensemble learning attribute selection entropy

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Importance of Features selection,attributes selection,Challenges and Future Directions for Medical Imaging Data:A Review

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Computer Modeling in Engineering & Sciences 2020年第10期125卷 315-344页

作者： Nazish Naheed Muhammad Shaheen Sajid Ali Khan Mohammed Alawairdhi Muhammad Attique Khan Faculty of Engineering&Information Technology Foundation University IslamabadIslamabadPakistan College of Computing and Informatics Saudi Electronic UniversityRayadhSaudi Arabia Department of Computer Science HITEC UniversityTaxilaPakistan

In the area of pattern recognition and machine learning,features play a key role in *** famous applications of features are medical imaging,image classification,and name a few *** the exponential growth of information investments in medical data repositories and health service provision,medical institutions are collecting large volumes of *** data repositories contain details information essential to support medical diagnostic decisions and also improve patient care *** the other hand,this growth also made it difficult to comprehend and utilize data for various *** results of imaging data can become biased because of extraneous features present in larger *** selection gives a chance to decrease the number of components in such large *** selection techniques,ousting the unimportant features and selecting a subset of components that produces prevalent characterization *** correct decision to find a good attribute produces a precise grouping model,which enhances learning pace and forecast *** paper presents a review of feature selection techniques and attributes selection measures for medical *** review is meant to describe feature selection techniques in a medical domainwith their pros and cons and to signify its application in imaging data and data mining *** review reveals the shortcomings of the existing feature and attributes selection techniques to multi-sourced ***,this review provides the importance of feature selection for correct classification of medical *** the end,critical analysis and future directions are provided.

关键词： Medical imaging imaging data feature selection data mining attribute selection medical challenges future directions

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Network Traffic Prediction Using Radial Kernelized-Tversky Indexes-Based Multilayer Classifier

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Computer Systems Science & Engineering 2022年第3期40卷 851-863页

作者： M.Govindarajan V.Chandrasekaran S.Anitha Department of Computer Applications Velalar College of Engineering and TechnologyErode638012India Department of Electronics and Communication Engineering Velalar College of Engineering and TechnologyErode638012India Department of Information Technology Kongu Engineering CollegePerunduraiIndia

Accurate cellular network traffic prediction is a crucial task to access Internet services for various devices at any *** the use of mobile devices,communication services generate numerous data for every *** the increasing dense population of data,traffic learning and prediction are the main components to substantially enhance the effectiveness of demand-aware resource allocation.A novel deep learning technique called radial kernelized LSTM-based connectionist Tversky multilayer deep structure learning(RKLSTM-CTMDSL)model is introduced for traffic prediction with superior accuracy and minimal time *** RKLSTM-CTMDSL model performs attribute selection and classification processes for cellular traffic *** this model,the connectionist Tversky multilayer deep structure learning includes multiple layers for traffic prediction.A large volume of spatial-temporal data are considered as an input-to-input ***,input data are transmitted to hidden layer 1,where a radial kernelized long short-term memory architecture is designed for the relevant attribute selection using activation function *** obtaining the relevant attributes,the selected attributes are given to the next *** index function is used in this layer to compute similarities among the training and testing traffic *** similarity index outcomes are given to the output *** value is used as basis to classify data as heavy network or normal ***,cellular network traffic prediction is presented with minimal error rate using the RKLSTM-CTMDSL *** evaluation proved that the RKLSTM-CTMDSL model outperforms conventional methods.

关键词： Cellular network traffic prediction connectionist Tversky multilayer deep structure learning attribute selection classification radial kernelized long short-term memory

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Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

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Computer Systems Science & Engineering 2023年第9期46卷 3121-3139页

作者： Khaled M.Alalayah Fatma S.Alrayes Jaber S.Alzahrani Khadija M.Alaidarous Ibrahim M.Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial *** Industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized the concepts of manufacturing and production *** industry 4.0,powerful IntrusionDetection Systems(IDS)play a significant role in ensuring network *** various intrusion detection techniques have been developed so far,it is challenging to protect the intricate data of *** is because conventional Machine Learning(ML)approaches are inadequate and insufficient to address the demands of dynamic IIoT ***,the existing Deep Learning(DL)can be employed to identify anonymous ***,the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection(HGSODLID)model for the IIoT *** presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful *** HGSO algorithm is employed for Feature selection(HGSO-FS)to reduce the curse of ***,Sparrow Search Optimization(SSO)is utilized with a Graph Convolutional Network(GCN)to classify and identify intrusions in the ***,the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN *** proposed HGSODL-ID model was experimentally validated using a benchmark dataset,and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.

关键词： Industrial IoT deep learning network security intrusion detection system attribute selection smart factory

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An Improved Bayesian with Application to Anti-Spam Email

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Journal of Electronic Science and Technology of China 2005年第1期3卷 30-33页

作者：詹川卢显良周旭侯孟书 School of Computer Science and Engineering UESTC Chengdu 610054 China

Along with the wide application of e-mail nowadays, many spam e-mails flood into people’s email-boxes and cause catastrophes to their study and life. In anti-spam e-mails campaign, we depend on not only legal measures but also technological approaches. The Bayesian classifier provides a simple and effective approach to discriminate classification. This paper presents a new improved Bayesian-based anti-spam e-mail filter. We adopt a way of attribute selection based on word entropy, use vector weights which are represented by word frequency, and deduce its corresponding formula. It is proved that our filter improves total performances apparently in our experiment.

关键词： word entropy Bayesian classification anti-spam e-mail filter attribute selection vector

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Less is More:Data Processing with SVM for Intrusion Detection

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Journal of Southwest Jiaotong University(English Edition) 2009年第1期17卷 9-15页

作者：肖海军洪帆王玲 School of Mathematics and Physics China University of Geosciences School of Computer Science and Technology Huazhong University of Science and Technology Business School Wuhan Institute of Technology

To improve the detection rate and lower down the false positive rate in intrusion detection system, dimensionality reduction is widely used in the intrusion detection system. For this purpose, a data processing （DP） with support vector machine （SVM） was built. Different from traditiona/ly identifying the redundant data before purging the audit data by expert knowledge or utilizing different kinds of subsets of the available 41-connection attributes to build a classifier, the proposed strategy first removes the attributes whose correlation with another attribute exceeds a threshold, and then classifies two sequence samples as one class while removing either of the two samples whose similarity exceeds a threshold. The results of performance experiments showed that the strategy of DP and SVM is superior to the other existing data reduction strategies （ e. g. , audit reduction, rule extraction, and feature selection）, and that the detection model based on DP and SVM outperforms those based on data mining, soft computing, and hierarchical principal component analysis neural networks.

关键词： Support vector machine Data processing attribute selection Similarity

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A NOVEL TRAFFIC CLASSIFICATION ALGORITHM USING MACHINE LEARNING

A NOVEL TRAFFIC CLASSIFICATION ALGORITHM USING MACHINE LEARN...

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2009 2nd IEEE International Conference on Broadband Network & Multimedia Technology（IC-BNMT2009）

作者： Liu Huixian Li Xiaojuan Capital Normal University Beijing

Internet traffic classification is of prime importance to the areas of network management and security monitoring,network planning,and QoS *** the Traditional Classifications depend on certain header fields （take port numbers for instance）.These port-based and payload-based approaches will be out of action when a lot of applications like P2P use dynamic port *** techniques and payload encryption requires a high amount of resource of computing and is easily infeasible in the protocol that unknown or *** paper describes a different level in network traffic-analysis using an unsupervised machine learning *** this approach flows are automatically classified by exploiting the different statistics characteristics of *** implement and estimate the efficiency and feasibility of our approach using data at different location of Internet.A new attribute selection method is put forward to determine optimal attribute set and evaluate the influence.

关键词： Machine-Learning (ML) Traffic Classification attribute selection

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一种基于高效分形维计算的快速属性相关性搜索算法

一种基于高效分形维计算的快速属性相关性搜索算法

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第二十二届中国数据库学术会议

作者：吴丹鲍玉斌刘运涛于戈孙焕良王大玲东北大学信息科学与工程学院

1引言在数据挖掘、文档分类和多媒体索引等新兴领域所处理的数据集规模巨大且复杂,表现为数据集的元组数目大、属性个数多,以及属性间关系复杂。这使得数据处理变得相当困难。人们除了需要发现数据集中具有代表性的数据和属性,还希望进... 详细信息

1引言在数据挖掘、文档分类和多媒体索引等新兴领域所处理的数据集规模巨大且复杂,表现为数据集的元组数目大、属性个数多,以及属性间关系复杂。这使得数据处理变得相当困难。人们除了需要发现数据集中具有代表性的数据和属性,还希望进一步发现属性之间的相关性。目前,已提出了许多

关键词： attribute selection Correlation-attribute group Fractal dimension Searching algorithm

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