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A self region based real-valued negative selection algorithm 被引量:1
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作者 张凤斌 王大伟 王胜文 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2008年第6期851-855,共5页
Point-wise negative selection algorithms,which generate their detector sets based on point of self data,have lower training efficiency and detection rate.To solve this problem,a self region based real-valued negative ... Point-wise negative selection algorithms,which generate their detector sets based on point of self data,have lower training efficiency and detection rate.To solve this problem,a self region based real-valued negative selection algorithm is presented.In this new approach,the continuous self region is defined by the collection of self data,the partial training takes place at the training stage according to both the radius of self region and the cosine distance between gravity of the self region and detector candidate,and variable detectors in the self region are deployed.The algorithm is tested using the triangle shape of self region in the 2-D complement space and KDD CUP 1999 data set.Results show that,more information can be provided when the training self points are used together as a whole,and compared with the point-wise negative selection algorithm,the new approach can improve the training efficiency of system and the detection rate significantly. 展开更多
关键词 artificial immune real-valued negative selection cluster analysis self region partial training
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Research on a randomized real-valued negative selection algorithm
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作者 张凤斌 王胜文 郝忠孝 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2006年第6期745-747,共3页
A real-valued negative selection algorithm with good mathematical foundation is presented to solve some of the drawbacks of previous approach. Specifically, it can produce a good estimate of the optimal number of dete... A real-valued negative selection algorithm with good mathematical foundation is presented to solve some of the drawbacks of previous approach. Specifically, it can produce a good estimate of the optimal number of detectors needed to cover the non-self space, and the maximization of the non-self coverage is done through an optimization algorithm with proven convergence properties. Experiments are performed to validate the assumptions made while designing the algorithm and to evaluate its performance. 展开更多
关键词 intrusion detection immune systems negative selection algorithm
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A recognition method of vibration parameter image based on improved immune negative selection algorithm for rotating machinery 被引量:4
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作者 窦唯 刘占生 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2009年第1期5-10,共6页
To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery usin... To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery using the negative selection mechanism of biology immune system. This method uses techniques of biology clone and learning mechanism to improve the negative selection algorithm to generate detectors possessing different monitoring radius, covers the abnormality space effectively, and avoids such problems as the low efficiency of generating detectors, etc. The result of an example applying the presented monitoring method shows that this method can solve the difficulty of obtaining fault samples preferably and extract the turbine state character effectively, it also can detect abnormality by causing various fault of the turbine and obtain the degree of abnormality accurately. The exact monitoring precision of abnormality indicates that this method is feasible and has better on-line quality, accuracy and robustness. 展开更多
关键词 artificial immune system negative selection algorithm abnormality monitor image recognition rotating machinery
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A negative selection algorithm with neighborhood representation 被引量:1
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作者 张凤斌 王大伟 王胜文 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2011年第3期74-78,共5页
This paper proposes a negative selection with neighborhood representation named as neighborhood negative selection algorithm.This algorithm employs a new representation method which uses the fully adjacent but mutuall... This paper proposes a negative selection with neighborhood representation named as neighborhood negative selection algorithm.This algorithm employs a new representation method which uses the fully adjacent but mutually disjoint neighborhoods to present the self samples and detectors.After normalizing the normal samples into neighborhood shape space,the algorithm uses a special matching rule similar as Hamming distance to train mature detectors at the training stage and detect anomaly at the detection stage.The neighborhood negative selection algorithm is tested using KDD CUP 1999 dataset.Experimental results show that the algorithm can prevent the negative effect of the dimension of shape space,and provide a more accuracy and stable detection performance. 展开更多
关键词 artificial immune negative selection NEIGHBORHOOD matching rule
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Fault Detection Using Negative Selection and Genetic Algorithms 被引量:3
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作者 Anam ABID Zia Ul HAQ Muhammad Tahir KHAN 《Instrumentation》 2019年第3期39-51,共13页
In this paper,negative selection and genetic algorithms are combined and an improved bi-objective optimization scheme is presented to achieve optimized negative selection algorithm detectors.The main aim of the optima... In this paper,negative selection and genetic algorithms are combined and an improved bi-objective optimization scheme is presented to achieve optimized negative selection algorithm detectors.The main aim of the optimal detector generation technique is maximal nonself space coverage with reduced number of diversified detectors.Conventionally,researchers opted clonal selection based optimization methods to achieve the maximal nonself coverage milestone;however,detectors cloning process results in generation of redundant similar detectors and inefficient detector distribution in nonself space.In approach proposed in the present paper,the maximal nonself space coverage is associated with bi-objective optimization criteria including minimization of the detector overlap and maximization of the diversity factor of the detectors.In the proposed methodology,a novel diversity factorbased approach is presented to obtain diversified detector distribution in the nonself space.The concept of diversified detector distribution is studied for detector coverage with 2-dimensional pentagram and spiral self-patterns.Furthermore,the feasibility of the developed fault detection methodology is tested the fault detection of induction motor inner race and outer race bearings. 展开更多
关键词 Detector Coverage Diversity Factor Fault Detection Genetic Algorithm negative selection Algorithm
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A Cuckoo Search Detector Generation-based Negative Selection Algorithm
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作者 Ayodele Lasisi Ali M.Aseere 《Computer Systems Science & Engineering》 SCIE EI 2021年第8期183-195,共13页
The negative selection algorithm(NSA)is an adaptive technique inspired by how the biological immune system discriminates the self from nonself.It asserts itself as one of the most important algorithms of the artificia... The negative selection algorithm(NSA)is an adaptive technique inspired by how the biological immune system discriminates the self from nonself.It asserts itself as one of the most important algorithms of the artificial immune system.A key element of the NSA is its great dependency on the random detectors in monitoring for any abnormalities.However,these detectors have limited performance.Redundant detectors are generated,leading to difficulties for detectors to effectively occupy the non-self space.To alleviate this problem,we propose the nature-inspired metaheuristic cuckoo search(CS),a stochastic global search algorithm,which improves the random generation of detectors in the NSA.Inbuilt characteristics such as mutation,crossover,and selection operators make the CS attain global convergence.With the use of Lévy flight and a distance measure,efficient detectors are produced.Experimental results show that integrating CS into the negative selection algorithm elevated the detection performance of the NSA,with an average increase of 3.52%detection rate on the tested datasets.The proposed method shows superiority over other models,and detection rates of 98%and 99.29%on Fisher’s IRIS and Breast Cancer datasets,respectively.Thus,the generation of highest detection rates and lowest false alarm rates can be achieved. 展开更多
关键词 negative selection algorithm detector generation cuckoo search OPTIMIZATION
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Clone Selection Algorithm with Niching Strategy for Computer Immune System 被引量:2
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作者 张雅静 侯朝桢 薛阳 《Journal of Beijing Institute of Technology》 EI CAS 2004年第2期169-173,共5页
A clone selection algorithm for computer immune system is presented. Clone selection principles in biological immune system are applied to the domain of computer virus detection. Based on the negative selection algori... A clone selection algorithm for computer immune system is presented. Clone selection principles in biological immune system are applied to the domain of computer virus detection. Based on the negative selection algorithm proposed by Stephanie Forrest, combining mutation operator in genetic algorithms and niching strategy in biology is adopted, the number of detectors is decreased effectively and the ability on self-nonself discrimination is improved. Simulation experiment shows that the algorithm is simple, practical and is adapted to the discrimination for long files. 展开更多
关键词 clone selection negative selection MUTATION computer immune system change detection
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Sampled Value Attack Detection for Busbar Differential Protection Based on a Negative Selection Immune System 被引量:1
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作者 Jun Mo Hui Yang 《Journal of Modern Power Systems and Clean Energy》 SCIE EI CSCD 2023年第2期421-433,共13页
Considering a variety of sampled value(SV)attacks on busbar differential protection(BDP)which poses challenges to conventional learning algorithms,an algorithm to detect SV attacks based on the immune system of negati... Considering a variety of sampled value(SV)attacks on busbar differential protection(BDP)which poses challenges to conventional learning algorithms,an algorithm to detect SV attacks based on the immune system of negative selection is developed in this paper.The healthy SV data of BDP are defined as self-data composed of spheres of the same size,whereas the SV attack data,i.e.,the nonself data,are preserved in the nonself space covered by spherical detectors of different sizes.To avoid the confusion between busbar faults and SV attacks,a self-shape optimization algorithm is introduced,and the improved self-data are verified through a power-frequency fault-component-based differential protection criterion to avoid false negatives.Based on the difficulty of boundary coverage in traditional negative selection algorithms,a self-data-driven detector generation algorithm is proposed to enhance the detector coverage.A testbed of differential protection for a 110 kV double busbar system is then established.Typical SV attacks of BDP such as amplitude and current phase tampering,fault replays,and the disconnection of the secondary circuits of current transformers are considered,and the delays of differential relay operation caused by detection algorithms are investigated. 展开更多
关键词 Cyberattack busbar differential protection(BDP) negative selection self-data-driven detector sampled value attacks internal faults
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How does male-male competition generate negative frequency-dependent selection and disruptive selection during speciation? 被引量:2
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作者 Peter D. DIJKSTRA Shana E. BORDER 《Current Zoology》 SCIE CAS CSCD 2018年第1期89-99,共11页
Natural selection has been shown to drive population differentiation and speciation. The role of sexual selection in this process is controversial; however, most of the work has centered on mate choice while the role ... Natural selection has been shown to drive population differentiation and speciation. The role of sexual selection in this process is controversial; however, most of the work has centered on mate choice while the role of male-male competition in speciation is relatively understudied. Here, we outline how male-male competition can be a source of diversifying selection on male competitive phenotypes, and how this can contribute to the evolution of reproductive isolation. We highlight how negative frequency-dependent selection (advantage of rare phenotype arising from stronger male-male competition between similar male phenotypes compared with dissimilar male pheno- types) and disruptive selection (advantage of extreme phenotypes) drives the evolution of diversity in competitive traits such as weapon size, nuptial coloration, or aggressiveness. We underscore that male-male competition interacts with other life-history functions and that variable male com- petitive phenotypes may represent alternative adaptive options. In addition to competition for mates, aggressive interference competition for ecological resources can exert selection on compet- itor signals. We call for a better integration of male-male competition with ecological interference competition since both can influence the process of speciation via comparable but distinct mecha- nisms. Altogether, we present a more comprehensive framework for studying the role of male-male competition in speciation, and emphasize the need for better integration of insights gained from other fields studying the evolutionary, behavioral, and physiological consequences of agonistic interactions. 展开更多
关键词 disruptive selection life-history trade-offs male-male competition negative frequency-dependent selection SPECIATION
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Clony color assay coupled with 5FOA negative selection greatly improves yeast three-hybrid library screening efficiency
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作者 Yuanzheng He Lei Bao Dinggan Liu 《Chinese Science Bulletin》 SCIE EI CAS 2001年第5期403-406,共4页
The recently developed yeast three-hybrid system is a powerful tool for analyzing RNA-protein interactions in vivo. However, large numbers of false positives are frequently met due to bait RNA-independent activation o... The recently developed yeast three-hybrid system is a powerful tool for analyzing RNA-protein interactions in vivo. However, large numbers of false positives are frequently met due to bait RNA-independent activation of the reporter gene in the library screening using this system. In this report, we coupled the colony color assay with the 5FOA (5-fluoroorotic acid) negative selection in the library screening, and found that this coupled method effectively eliminated bait RNA-independent false positives and hence greatly improved library screening efficiency. We used this method successfully in isolation of cDNA of an RNA-binding protein that might play important roles in certain cellular process. This improvement will facilitate the use of the yeast three-hybrid system in analyzing RNA-protein interaction. 展开更多
关键词 COLONY COLOR ASSAY 5FOA negative selection YEAST three-hybrid system RNA binding protein.
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Negative Selection of Written Language Using Character Multiset Statistics
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作者 Matti Pll Timo Honkela 《Journal of Computer Science & Technology》 SCIE EI CSCD 2010年第6期1256-1266,共11页
We study the combination of symbol frequence analysis and negative selection for anomaly detection of discrete sequences where conventional negative selection algorithms are not practical due to data sparsity.Theoreti... We study the combination of symbol frequence analysis and negative selection for anomaly detection of discrete sequences where conventional negative selection algorithms are not practical due to data sparsity.Theoretical analysis on ergodic Markov chains is used to outline the properties of the presented anomaly detection algorithm and to predict the probability of successful detection.Simulations are used to evaluate the detection sensitivity and the resolution of the analysis on both generated artificial data and real-world language data including the English Wikipedia.Simulation results on large reference corpora are used to study the effects of the assumptions made in the theoretical model in comparison to real-world data. 展开更多
关键词 negative selection anomaly detection frequency analysis
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基于动态选择性集成学习的供应链产销协商优化策略 被引量:1
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作者 武玉英 严勇 蒋国瑞 《计算机工程》 CAS CSCD 北大核心 2017年第5期191-196,共6页
针对当前Agent产销协商自适应学习效果差及协商环境动态变化的现状,考虑动态协商环境中的冲突水平、合作可能性、协商剩余时间对谈判的影响,利用熵值法确定3个影响因素的权重并进行线性加权。结合当前协商议题的差异性,构建基于动态选... 针对当前Agent产销协商自适应学习效果差及协商环境动态变化的现状,考虑动态协商环境中的冲突水平、合作可能性、协商剩余时间对谈判的影响,利用熵值法确定3个影响因素的权重并进行线性加权。结合当前协商议题的差异性,构建基于动态选择性集成学习的让步幅度预测模型,并提出供应链产销协商优化策略。实验结果表明,与单学习机协商策略相比,该策略提高了Agent自适应学习成功率及联合效用,并且能确保供应链产销双方受益,实现合作双方互利互赢的局面。 展开更多
关键词 动态选择性集成学习 动态协商环境 Agent产销协商 自适应学习 熵值法
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对“冗余否定”一例的再思考 被引量:1
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作者 刘承峰 《枣庄师范专科学校学报》 2002年第4期83-86,共4页
本文通过对“怀疑……不”和“怀疑”表义的分析 ,认为 ,“怀疑……不”中的“不”并不是一贯所认为的“冗余否定”标记 ,而只是一般的否定标记。“怀疑“本身就具有肯定、否定两个对立义项 ,这是造成人们认为“不”是“冗余否定”标记... 本文通过对“怀疑……不”和“怀疑”表义的分析 ,认为 ,“怀疑……不”中的“不”并不是一贯所认为的“冗余否定”标记 ,而只是一般的否定标记。“怀疑“本身就具有肯定、否定两个对立义项 ,这是造成人们认为“不”是“冗余否定”标记的原因。“不”的存在是具有不可替代的价值的。 展开更多
关键词 冗余否定 歧义 语感 汉语 句式
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“非得+VP”格式的多角度考察
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作者 王天佑 《五邑大学学报(社会科学版)》 2011年第1期88-91,94,共4页
从语义表达的角度,可将现代汉语"非得+VP"格式分为"意愿之必欲"、"情势之必须"和"推断之必然"三种类型。考察发现,它们都蕴涵"肯/否定"的语用功能,同时在表达语义和生成句式上又有... 从语义表达的角度,可将现代汉语"非得+VP"格式分为"意愿之必欲"、"情势之必须"和"推断之必然"三种类型。考察发现,它们都蕴涵"肯/否定"的语用功能,同时在表达语义和生成句式上又有差异。依据三类"非得+VP"格式的句式特点及语义差异分析,可对副词"非得"进行一次小类划分。 展开更多
关键词 非得+VP 肯/否定 取舍 推论 主观标准 客观标准
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基于免疫学架构的文本信息检索模型 被引量:4
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作者 赵孟德 《华东理工大学学报(自然科学版)》 CAS CSCD 北大核心 2007年第B06期132-136,共5页
主要介绍一种新的模型——基于计算机免疫模型的信息检索模型。计算机免疫模型是对人体免疫系统的一种模拟,在这个模型中,主要结合使用传统的词向量模型和新的计算机免疫模型来构建一个智能搜索引擎的内核模型。它具有可以自动学习、... 主要介绍一种新的模型——基于计算机免疫模型的信息检索模型。计算机免疫模型是对人体免疫系统的一种模拟,在这个模型中,主要结合使用传统的词向量模型和新的计算机免疫模型来构建一个智能搜索引擎的内核模型。它具有可以自动学习、高效响应、自动分类、自动识别模式等特点,可以应用于多种信息检索。该模型能通过免疫算法提高搜索引擎的搜索精确度和减少响应时间。 展开更多
关键词 信息检索 计算机免疫模型 否定选择算法 遗传算法 克隆选择算法 免疫细胞
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Multi-agent decision support system for missile defense based on improved PSO algorithm 被引量:5
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作者 Zilong Cheng Li Fan Yulin Zhang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2017年第3期514-525,共12页
Ballistic missile defense system (BMDS) is important for its special role in ensuring national security and maintaining strategic balance. Research on modeling and simulation of the BMDS beforehand is essential as dev... Ballistic missile defense system (BMDS) is important for its special role in ensuring national security and maintaining strategic balance. Research on modeling and simulation of the BMDS beforehand is essential as developing a real one requires lots of manpower and resources. BMDS is a typical complex system for its nonlinear, adaptive and uncertainty characteristics. The agent-based modeling method is well suited for the complex system whose overall behaviors are determined by interactions among individual elements. A multi-agent decision support system (DSS), which includes missile agent, radar agent and command center agent, is established based on the studies of structure and function of BMDS. Considering the constraints brought by radar, intercept missile, offensive missile and commander, the objective function of DSS is established. In order to dynamically generate the optimal interception plan, the variable neighborhood negative selection particle swarm optimization (VNNSPSO) algorithm is proposed to support the decision making of DSS. The proposed algorithm is compared with the standard PSO, constriction factor PSO (CFPSO), inertia weight linear decrease PSO (LDPSO), variable neighborhood PSO (VNPSO) algorithm from the aspects of convergence rate, iteration number, average fitness value and standard deviation. The simulation results verify the efficiency of the proposed algorithm. The multi-agent DSS is developed through the Repast simulation platform and the constructed DSS can generate intercept plans automatically and support three-dimensional dynamic display of missile defense process. 展开更多
关键词 agent-based modeling missile defense system decision support system (DSS) variable neighborhood negative selection particle swarm optimization (PSO)
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Smartphone Malware Detection Model Based on Artificial Immune System 被引量:1
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作者 WU Bin LU Tianliang +2 位作者 ZHENG Kangfeng ZHANG Dongmei LIN Xing 《China Communications》 SCIE CSCD 2014年第A01期86-92,共7页
In order to solve the problem that me traditional signature-based detection technology cannot effectively detect unknown malware, we propose in this study a smartphone malware detection model (SP-MDM) based on artif... In order to solve the problem that me traditional signature-based detection technology cannot effectively detect unknown malware, we propose in this study a smartphone malware detection model (SP-MDM) based on artificial immune system, in which static malware analysis and dynamic malware analysis techniques are combined, and antigens are generated by encoding the characteristics extracted from the malware. Based on negative selection algorithm, the mature detectors are generated. By introducing clonal selection algorithm, the detectors with higher affinity are selected to undergo a proliferation and somatic hyper-mutation process, so that more excellent detector offspring can be generated. Experimental result shows that the detection model has a higher detection rate for unknown smartphone malware, and better detection performance can be achieved by increasing the clone generation. 展开更多
关键词 artificial immune system smartphonemalware DETECTION negative selection clonalselection
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A Novel Radius Adaptive Based on Center-Optimized Hybrid Detector Generation Algorithm 被引量:1
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作者 Jinyin Chen 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2020年第6期1627-1637,共11页
Negative selection algorithm(NSA)is one of the classic artificial immune algorithm widely used in anomaly detection.However,there are still unsolved shortcomings of NSA that limit its further applications.For example,... Negative selection algorithm(NSA)is one of the classic artificial immune algorithm widely used in anomaly detection.However,there are still unsolved shortcomings of NSA that limit its further applications.For example,the nonselfdetector generation efficiency is low;a large number of nonselfdetector is needed for precise detection;low detection rate with various application data sets.Aiming at those problems,a novel radius adaptive based on center-optimized hybrid detector generation algorithm(RACO-HDG)is put forward.To our best knowledge,radius adaptive based on center optimization is first time analyzed and proposed as an efficient mechanism to improve both detector generation and detection rate without significant computation complexity.RACO-HDG works efficiently in three phases.At first,a small number of self-detectors are generated,different from typical NSAs with a large number of self-sample are generated.Nonself-detectors will be generated from those initial small number of self-detectors to make hybrid detection of self-detectors and nonself-detectors possible.Secondly,without any prior knowledge of the data sets or manual setting,the nonself-detector radius threshold is self-adaptive by optimizing the nonself-detector center and the generation mechanism.In this way,the number of abnormal detectors is decreased sharply,while the coverage area of the nonself-detector is increased otherwise,leading to higher detection performances of RACOHDG.Finally,hybrid detection algorithm is proposed with both self-detectors and nonself-detectors work together to increase detection rate as expected.Abundant simulations and application results show that the proposed RACO-HDG has higher detection rate,lower false alarm rate and higher detection efficiency compared with other excellent algorithms. 展开更多
关键词 Artificial immunity center optimized hybrid detect negative detector negative selection algorithm(NSA) radius adaptive
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Can sexual selection and disassortative mating contribute to the maintenance of a shell color polymorphism in an inter- tidal marine snail? 被引量:1
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作者 Emilio ROLALVAREZ Maria SAURA Angel E DIZ Marfa JOSE RIVAS MargaALVAREZ Beatriz CORTES Alicia de COO Daniel ESTEVEZ Laura IGLESIAS 《Current Zoology》 SCIE CAS CSCD 2012年第3期463-474,共12页
Littorina fabalis is an intertidal snail commonly living on the brown algae Fucus vesiculosus and showing frequent shell-color polymorphisms in the wild. The evolutionary mechanism underlying this polymorphism is curr... Littorina fabalis is an intertidal snail commonly living on the brown algae Fucus vesiculosus and showing frequent shell-color polymorphisms in the wild. The evolutionary mechanism underlying this polymorphism is currently unknown. Shell color variation was studied in mated and non-mated specimens of this species from different microareas in one locality from NW Spain, in order to estimate sexual selection and assortative mating that may (still) be operating in this population. The analyses across microareas allowed us to investigate frequency-dependent selection and assortative mating components, mechanisms that could maintain the polymorphism. The presence of shell scars caused by crab attacks, an environmental variable not related with sexual selection or assortative mating, was used as experimental control. This study provides new evidence of significant disas- sortative mating and some degree of sexual selection against some shell colors, supporting the results found 21 years ago in a similar study, i.e. in the same species and locality. The similarity of these estimates during the studied period suggests that this experimental approach is consistent and valid to be extended to other populations and organisms. In addition, sexual selection and assortative mating estimates did not change across microareas differing in shell color frequencies, suggesting than the polymor- phism can not be maintained by a frequency-dependent (sexual selection-based) mechanism. Our main hypothesis is that negative assortative mating could contribute to the maintenance of the polymorphism, perhaps by males showing distinct female color preferences when searching for mates [Current Zoology 58 (3): 463-474, 2012]. 展开更多
关键词 Fitness estimate Mate choice Mate propensity negative assortative mating Sexual selection Frequency-dependentselection
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An Improved Artificial Immune System-Based Network Intrusion Detection by Using Rough Set 被引量:1
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作者 Junyuan Shen Jidong Wang Hao Ai 《Communications and Network》 2012年第1期41-47,共7页
With theincreasing worldwide network attacks, intrusion detection (ID) hasbecome a popularresearch topic inlast decade.Several artificial intelligence techniques such as neural networks and fuzzy logichave been applie... With theincreasing worldwide network attacks, intrusion detection (ID) hasbecome a popularresearch topic inlast decade.Several artificial intelligence techniques such as neural networks and fuzzy logichave been applied in ID. The results are varied. Theintrusion detection accuracy is themain focus for intrusion detection systems (IDS). Most research activities in the area aiming to improve the ID accuracy. In this paper, anartificial immune system (AIS) based network intrusion detection scheme is proposed. An optimized feature selection using Rough Set (RS) theory is defined. The complexity issue is addressed in the design of the algorithms. The scheme is tested on the widely used KDD CUP 99 dataset. The result shows that theproposed scheme outperforms other schemes in detection accuracy. 展开更多
关键词 INTRUSION Detection negative selection Artificial IMMUNE System KDD CUP 99
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