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information gain based sensor search scheduling for low-earth orbit constellation estimation

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Journal of Systems Engineering and Electronics 2011年第6期22卷 926-932页

作者： Bo Wang Jun Li Wei An Yiyu Zhou School of Electronics and Engineering National University of Defense Technology Changsha 410073 R R. China Institute of Radar and Electronic Warfare Equipment Academy of Air Force Beijing 100085 P. R. China

This paper addresses the problem of sensor search scheduling in the complicated space environment faced by the low-earth orbit *** search scheduling methods based on the commonly used information gain are compared via simulations *** a novel search scheduling method in the scenarios of uncertainty observation is proposed based on the global Shannon information gain and beta density based uncertainty *** results indicate that the beta density model serves a good option for solving the problem of target acquisition in the complicated space environments.

关键词： low-earth orbit constellation sensor network scheduling algorithm information gain acquisition.

Hybrid Method Based on information gain and Support Vector Machine for Gene Selection in Cancer Classi?cation

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Genomics, Proteomics & Bioinformatics 2017年第6期15卷 389-395页

作者： Lingyun Gao Mingquan Ye Xiaojie Lu Daobin Huang School of Medical information Wannan Medical CollegeWuhu 241002China

It remains a great challenge to achieve sufficient cancer classification accuracy with the entire set of genes, due to the high dimensions, small sample size, and big noise of gene expression data. We thus proposed a hybrid gene selection method, information gain-Support Vector Machine （IG-SVM） in this study. IG was initially employed to filter irrelevant and redundant genes. Then, further removal of redundant genes was performed using SVM to eliminate the noise in the datasets more effectively. Finally, the informative genes selected by IG-SVM served as the input for the LIBSVM classifier. Compared to other related algorithms, IG-SVM showed the highest classification accuracy and superior performance as evaluated using five cancer gene expression datasets based on a few selected genes. As an example, IG-SVM achieved a classification accuracy of 90.32% for colon cancer, which is difficult to be accurately classified, only based on three genes including CSRP1, MYLg, and GUCA2B.

关键词： Gene selection Cancer classification information gain Support vector machine Small sample size with highdimension

A novel business analytics approach and case study-fuzzy associative classifier based on information gain and rule-covering

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Journal of Management Analytics 2014年第1期1卷 1-19页

作者： Yue Ma Guoqing Chena Qiang Wei Research Center for Contemporary Management Key Research Institute of Humanities and Social Sciences at Universities School of Economics and Management Tsinghua UniversityBeijing 100084China

Associative classification has attracted remarkable research attention for business analytics in recent years due to its merits in accuracy and *** is deemed meaningful to construct an associative classifier with a compact set of rules(i.e.,compactness),which is easy to understand and use in decision *** paper presents a novel approach to fuzzy associative classification(namely gain-based Fuzzy Rule-Covering classification,GFRC),which is a fuzzy extension of an effective classifier *** GFRC,two desirable strategies are introduced to enhance the compactness with *** strategy is fuzzy partitioning for data discretization to cope with the‘sharp boundary problem’,in that simulated annealing is incorporated based on the information entropy measure;the other strategy is a data-redundancy resolution coupled with the rulecovering *** experiments show that GFRC had good accuracy,and was significantly advantageous over other classifiers in ***,GFRC is applied to a real-world case for predicting the growth of sellers in an electronic marketplace,illustrating the classification effectiveness with linguistic rules in business decision support.

关键词： associative classification information gain fuzzy partitioning simulated annealing rule-covering

Cluster DetectionMethod of Endogenous Security Abnormal Attack Behavior in Air Traffic Control Network

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Computers, Materials & Continua 2024年第5期79卷 2523-2546页

作者： Ruchun Jia Jianwei Zhang Yi Lin Yunxiang Han Feike Yang College of Computer Science Sichuan UniversityChengdu610065China Equipment Management and Unmanned Aerial Vehicle College of Air Force Engineering University Air Force Engineering UniversityXi’an710051China

In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security *** feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature *** expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature *** autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior *** on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is ***,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering ***,the new average value of all feature objects in each cluster is recalculated as the new cluster ***,it traverses all objects in a cluster of ATC network security attack behavior feature ***,the cluster detection of ATC network security attack behavior is completed by the computation of objective *** experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative *** experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.

关键词： Air traffic control network security attack behavior cluster detection behavioral characteristics information gain cluster threshold automatic encoder

Attribute Weighted Naïve Bayes Classifier

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Computers, Materials & Continua 2022年第4期71卷 1945-1957页

作者： Lee-Kien Foo Sook-Ling Chua Neveen Ibrahim Multimedia University Cyberjaya63100Malaysia

The naïve Bayes classifier is one of the commonly used data mining methods for *** its simplicity,naïve Bayes is effective and computationally *** the strong attribute independence assumption in the naïve Bayes classifier makes it a tractable method for learning,this assumption may not hold in real-world *** enhancements to the basic algorithm have been proposed in order to alleviate the violation of attribute independence *** these methods improve the classification performance,they do not necessarily retain the mathematical structure of the naïve Bayes model and some at the expense of computational *** approach to reduce the naïvetéof the classifier is to incorporate attribute weights in the conditional *** this paper,we proposed a method to incorporate attribute weights to naïve *** evaluate the performance of our method,we used the public benchmark *** compared our method with the standard naïve Bayes and baseline attribute weighting *** results show that our method to incorporate attribute weights improves the classification performance compared to both standard naïve Bayes and baseline attribute weighting methods in terms of classification accuracy and F1,especially when the independence assumption is strongly violated,which was validated using the Chi-square test of independence.

关键词： Attribute weighting naïve Bayes Kullback-Leibler information gain classification

Improved Dragonfly Optimizer for Intrusion Detection Using Deep Clustering CNN-PSO Classifier

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Computers, Materials & Continua 2022年第3期70卷 5949-5965页

作者： K.S.Bhuvaneshwari K.Venkatachalam S.Hubalovsky P.Trojovsky P.Prabu Department of Computer Sceince and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Mathematics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Sceince CHRIST(Deemed to be University)Bangalore560074India

With the rapid growth of internet based services and the data generated on these services are attracted by the attackers to intrude the networking services and *** on the characteristics of these intruders,many researchers attempted to aim to detect the intrusion with the help of automating ***,the large volume of data is generated and transferred through network,the security and performance are remained an ***(Intrusion Detection System)was developed to detect and prevent the intruders and secure the network *** performance and loss are still an issue because of the features space grows while detecting the *** this paper,deep clustering based CNN have been used to detect the intruders with the help of Meta heuristic algorithms for feature selection and *** proposed system includes three phases such as preprocessing,feature selection and *** the first phase,KDD dataset is preprocessed by using Binning normalization and Eigen-PCA based discretization *** second phase,feature selection is performed by using information gain based Dragonfly Optimizer(IGDFO).Finally,Deep clustering based Convolutional Neural Network(CCNN)classifier optimized with Particle Swarm Optimization(PSO)identifies intrusion attacks *** clustering loss and network loss can be reduced with the optimization *** evaluate the proposed IDS model with the NSL-KDD dataset in terms of evaluation *** experimental results show that proposed system achieves better performance compared with the existing system in terms of accuracy,precision,recall,f-measure and false detection rate.

关键词： Intrusion detection system binning normalization deep clustering convolutional neural network information gain dragonfly optimizer

Enhanced Entropy-Based Resource Searching in Unstructured P2P Networks

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Chinese Journal of Electronics 2015年第2期24卷 229-235页

作者： GONG Weihua JIN Rong YANG Lianghuai HUANG Decai School of Computer Science and Technology Zhejiang University of Technology Key Laboratory of Visual Media Intelligent Process Technology of Zhejiang Province School of Informatics and Electronics Zhejiang Sci-Tech University

How to find desired resources efficiently and accurately is one fundamental challenge of any unstructured P2 P networks, which is mainly involved some difficulties in the P2 P overlay topology, data representation in peers and routing mechanism. In this paper, we address the issue of resilient routing in unstructured P2 P networks. An efficient algorithm called Query routing tree(QRT) based on maximum mutual information is proposed to improve the performance of resource searching, which has tightly associated the resource contents of peers with the logical links in P2 P network that makes the query messages forwarded more effectively in similar peers and can hit more target resources faster. Additionally, we present an optimized routing scheme with the query conditions taken into account, to obtain the optimal routing tree with the minimal information gain from the candidate tree set so as to adapt to different query types more flexibly. The simulation results show the proposed QRT can reduce the search cost more effectively and maintain higher targets hit rate than existing typical algorithms such as Flooding, k-RW and APS. Finally, our optimized scheme is also proved to conduct high searching performance with nicer self-adaptability and scalability in unstructured P2 P networks.

关键词： Peer-to-peer(P2P) Resource searching Entropy Query routing tree information gain

Cued search algorithm with uncertain detection performance for phased array radars

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Journal of Systems Engineering and Electronics 2013年第6期24卷 938-945页

作者： Jianbin Lu Hui Xiao Zemin Xi Mingmin Zhang Electronic Engineering College Naval University of Engineering Air Force Early Warning Academy

A cued search algorithm with uncertain detection performance is proposed for phased array radars. Firstly, a target search model based on the information gain criterion is presented with known detection performance, and the statistical characteristic of the detection probability is calculated by using the fluctuant model of the target radar cross section （RCS）. Secondly, when the detection probability is completely unknown, its probability density function is modeled with a beta distribution, and its posterior probability distribution with the radar observation is derived based on the Bayesian theory. Finally simulation results show that the cued search algorithm with a known RCS fluctuant model can achieve the best performance, and the algorithm with the detection probability modeled as a beta distribution is better than that with a random selected detection probability because the model parameters can be updated by the radar observation to approach to the real value of the detection probability.

关键词： phased array radar detection performance cued search information gain beta distribution

Key Symptoms Selection for Two Major SyndromesDiagnosis of Chinese Medicine in Chronic Hepatitis B

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Chinese Journal of Integrative Medicine 2017年第4期23卷 253-260页

作者： ZHAO Yu KANG Hong PENG Jing-hua XU Lin CAO Zhi-wei HU Yi-yang Institute of Liver Disease Shuguang Hospital Shanghai University of Traditional Chinese Medicine Shanghai 201203 China School of Life Sciences and Technology Tongji University Shanghai 200092 China E-institute of Shanghai Municipal Education Commission Shanghai 201203 China

To identify key symptoms of two major syndromes in chronic hepatitis B (CHB), which can be the clinical evidence for Chinese medicine (CM) doctors to make decisions. Standardization scales on diagnosis for CHB in CM were designed including physical symptoms, tongue and pulse appearance. The total of 695 CHB cases with dampness-heat (DH) syndrome or Pi (Spleen) deficiency (SD) syndrome were collected for feature selection and modeling, another 275 CHB patients were collected in different locations for validation. Key symptoms were selected based on modified information gain (IG), and 5 classifiers were applied to assist with models training and validation. Classification accuracy and area under receiver operating characteristic curves (AUC) were evaluated. (1) Thirteen DH syndrome key symptoms and 13 SD syndrome key symptoms were selected from original 125 symptoms; (2) The key symptoms could achieve similar or better diagnostic accuracy than the original total symptoms; (3) In the validation phase, the key symptoms could identify syndromes effectively, especially in DH syndrome, which average prediction accuracy on 5 classifiers could achieve 0.864 with the average AUC 0.772. The selected key symptoms could be simple DH and SD syndromes diagnostic elements applied in clinical directly. (Registration N0.: ChiCTR-DCC-10000759).

关键词： Chinese medicine syndrome chronic hepatitis B information gain