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international conference on Wireless Communications, Networking and Mobile Computing (WiCom)

作者： Sixing Yin Shufang Li Jixin Yin

As the wireless services developed rapidly in the recent years, a diversity of wireless services emerge such that radio environment becomes more and more complicated. Radio Spectrum security is now attached with great importance. Real time spectrum anomalies detection is vital for increasing demand on security to ensure that wireless services function on the rails. Malicious radio events, such as illegal channel occupation, happened frequently in the recent years, which result in severe interference to the normal radio spectrum usage. there were anomalies detection approaches in different areas proposed to conquer such malicious events. However, those malicious events usually happen in a short interval, this increases the demand on instantaneous responds for real-time events, and the complexity of previous approaches makes them insufficient to handle the real time task. In this paper, a new approach for anomalies detection in spectrum monitoring is proposed. Distinct from previous anomalies detection methods, both temporal and spectral information are taken into account and utilized to find out the potential anomalies. Meanwhile, an adaptive learning ability is proposed along to respond to the real-time change of radio environment. To analyze spectrum measurement data with high dimension, Mahalanobis distance is applied to disclose potential anomalies according to the historical pattern of radio spectrum. Methodology analysis and real case study have been performed to validate the detection effectiveness in practice.

关键词： data mining Monitoring Communication system security data security Rails Interference Event detection data analysis pattern analysis Performance analysis

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mining bilingual data from the web with adaptively learnt patterns 09

Mining bilingual data from the web with adaptively learnt pa...

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Proceedings of the Joint conference of the 47th Annual Meeting of the ACL and the 4th international Joint conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2

作者： Long Jiang Shiquan Yang Ming Zhou Xiaohua Liu Qingsheng Zhu Microsoft Research Asia Beijing P.R. China Chongqing University Chongqing P.R. China

ISBN: (纸本)9781932432466

mining bilingual data (including bilingual sentences and terms) from the Web can benefit many NLP applications, such as machine translation and cross language information retrieval. In this paper, based on the observation that bilingual data in many web pages appear collectively following similar patterns, an adaptive pattern-based bilingual data mining method is proposed. Specifically, given a web page, the method contains four steps: 1) preprocessing: parse the web page into a DOM tree and segment the inner text of each node into snippets; 2) seed mining: identify potential translation pairs (seeds) using a word based alignment model which takes both translation and transliteration into consideration; 3) pattern learning: learn generalized patterns with the identified seeds; 4) pattern based mining: extract all bilingual data in the page using the learned patterns. Our experiments on Chinese web pages produced more than 7.5 million pairs of bilingual sentences and more than 5 million pairs of bilingual terms, both with over 80% accuracy.

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Generalized Locally Linear Embedding Based on Local Reconstruction Similarity

Generalized Locally Linear Embedding Based on Local Reconstr...

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5th international conference on Fuzzy Systems and Knowledge Discovery

作者： Zeng, Xianhua Luo, Siwei China W Normal Univ Sch Comp Nanchong 637002 Peoples R China Beihang Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China

ISBN: (纸本)9780769533056

Manifold learning has currently become a hot issue in the field of machine learning, pattern recognition and data mining. Locally linear embedding (LLE) is one of several promising manifold learning methods. But ordinary LLE can not distinguish effectively the low-dimensional embeddings of noise data. By introducing the reconstruction similarity into LLE, this paper proposes a generalized locally linear embedding algorithm based on local reconstruction similarity. Experimental results show on Columbia object image data that the new generalized version is superior to LLE in revealing the visualization of high-dimensional image dataset containing noise images.

关键词： pattern recognition

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Information granulation based data mining approach for classifying imbalanced data

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INFORMATION SCIENCES 2008年第16期178卷 3214-3227页

作者： Chen, Mu-Chen Chen, Long-Sheng Hsu, Chun-Chin Zeng, Wei-Rong Natl Chiao Tung Univ Inst Traff & Transportat Sect 1 Taipei 10012 Taiwan Chaoyang Univ Technol Dept Informat Management Wufong Township 41349 Taichung County Taiwan Chaoyang Univ Technol Dept Ind Engn & Management Wufong Township 41349 Taichung County Taiwan Entie Commercial Bank Informat Management Dept Taipei Taiwan

Recently, the class imbalance problem has attracted much attention from researchers in the field of data mining. When learning from imbalanced data in which most examples are labeled as one class and only few belong to another class, traditional data mining approaches do not have a good ability to predict the crucial minority instances. Unfortunately, many real world data sets like health examination, inspection, credit fraud detection, spam identification and text mining all are faced with this situation. In this study, we present a novel model called the "Information Granulation Based data mining Approach" to tackle this problem. the proposed methodology, which imitates the human ability to process information, acquires knowledge from Information Granules rather then from numerical data. this method also introduces a Latent Semantic Indexing based feature extraction tool by using Singular Value Decomposition, to dramatically reduce the data dimensions. In addition, several data sets from the UCI machine learning Repository are employed to demonstrate the effectiveness of our method. Experimental results show that our method can significantly increase the ability of classifying imbalanced data. (c) 2008 Elsevier Inc. All rights reserved.

关键词： information granulation granular computing data mining latent semantic indexing imbalanced data feed-forward neural network

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Unsupervised Sequential Forward Dimensionality Reduction Based On Fractal

Unsupervised Sequential Forward Dimensionality Reduction Bas...

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5th international conference on Fuzzy Systems and Knowledge Discovery

作者： Yan, GuangHui Liu, LiSong Du, LinNa Yang, XiaXia Ma, ZhiCheng Lanzhou Jiaotong Univ Sch Informat & Elect Engn Lanzhou 730070 Peoples R China Gansu Elect Power Informat & Commun Cent Lanzhou 730050 Peoples R China

ISBN: (纸本)9780769533056

Dimensionality reduction has long been an active research topic within statistics, pattern recognition, machine learning and data mining. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. In this paper we transform the attribute selection problem into the optimization problem which tries to find the attribute subset with the maximal fractal dimension and the attribute number restriction simultaneously. In order to avoid exhaustive search in the huge attribute subset space we integrate the individual attribute priority with attribute subset evaluation for dimensionality reduction and propose the unsupervised Sequential Forward Fractal Dimensionality Reduction(SFFDR) algorithm. Our experiments on synthetic and real datasets show that the algorithm proposed can get the satisfied resulting attribute subset with a rather low time complexity.

关键词： data mining

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A COMPARATIVE STUDY OF RBF NEURAL NETWORK AND SVM CLASSIFICATION TECHNIQUES PERFORMED ON REAL data FOR DRINKING WATER QUALITY

A COMPARATIVE STUDY OF RBF NEURAL NETWORK AND SVM CLASSIFICA...

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5th international Multi-conference on Systems, Signals and Devices

作者： Bouamar, Mohamed Ladial, Mohamed LASS Laboratory of Analysis of Signals and Systems M'sila University Algeria

ISBN: (纸本)9781424422050

the control and monitoring of drinking water is becoming more and more interesting because of its effects on human life. Many techniques were developed in this field in order to ameliorate this process control attending to rigorous follow-ups of the quality of this vital resource. Several methods were implemented to achieve this goal. In this paper, a comparative study of two techniques resulting from the field of the artificial intelligence namely: RBF Neural Network (RBF-NN) and Support Vector machine (SVM), is presented Developed from the statistical learning theory, these methods display optimal training performances and generalization in many fields of application, among others the field of pattern recognition. Applied as classification tools, these techniques should ensure within a multi-sensor monitoring system, a direct and quasi permanent control of water quality. In order to evaluate their performances, a simulation using real data, corresponding to the recognition rate, the training time, and the robustness, is carried out. To validate their functionalities, an application is presented.

关键词： Water resources quality control pattern classification Robustness Simulation

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Active BSVM learning for relevance feedback in content-based sketch retrieval

Active BSVM learning for relevance feedback in content-based...

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5th IASTED international conference on Signal Processing, pattern recognition, and Applications, SPPRA 2008

作者： Liang, Shuang Sun, Zhengxing State Key Lab. for Novel Software Technology Nanjing University Nanjing 210093 China Department of Computer Science and Technology Nanjing University Nanjing 210093 China

ISBN: (纸本)9780889867178

the availability of relevance feedback is held back by the problem of the imbalance and limited size of labeled training data, as well as the real-time requirement of online interaction demands. In this paper, we propose a relevance feedback algorithm called active biased SVM (BSVM) learning, in which biased classification and active learning are employed to address these difficulties. the algorithm is applied to content-based sketch retrieval (CBSR), and the experiments prove both the effectiveness and efficiency of the proposed approach.

关键词： Active learning Biased SVM (BSVM) Content-based sketch retrieval (CBSR) Relevance feedback

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5th international conference on Soft Computing as Transdisciplinary Science and Technology, CSTST '08 - Proceedings

5th International Conference on Soft Computing as Transdisci...

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5th international conference on Soft Computing As Transdisciplinary Science and Technology, CSTST '08

ISBN: (纸本)9781605580463

the proceedings contain 111 papers. the topics discussed include: evolutionary multiobjective optimization and multiobjective fuzzy system design;chance discovery as value sensing by data based meta cognition;building classification rules for case based classifier using fuzzy sets and formal concept analysis;intelligent hybrid system for pattern recognition and classification;hybrid approach using ant colony optimization and fuzzy logic to solve multi-criteria hybrid flow shop scheduling problem;improving performance of intrusion detection system by applying a new machine learning strategy;network security simulation and evaluation;extending web applications with a lightweight zero knowledge proof authentication;implementation of a neural-based navigation approach on indoor and outdoor mobile robots;a human-machine interface design for direct rehabilitation using a rehabilitation robot;and Persian on-line handwritten character recognition by RCE spatio-temporal neural network.

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VTK: Vertical mining of Top-Rank-K Frequent patterns

VTK: Vertical Mining of Top-Rank-K Frequent Patterns

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5th international conference on Fuzzy Systems and Knowledge Discovery

作者： Fang, Guo-Dong Deng, Zhi-Hong Peking Univ Key Lab Machine Percept Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China

ISBN: (纸本)9780769533056

mining Top-Rank-K frequent patterns is a new topic in frequent pattern mining. In this paper, we propose a new mining algorithm called VTK, Vertical mining of Top-Rank-K Frequent patterns, to mining Top-Rank-K frequent patterns using some Vertical skills. Our performance study shows that the VTK method is more efficient and scalable for mining both synthetic datasets and real datasets than the algorithms proposed before.

关键词： data mining

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Feature selection based on scatter degree

Feature selection based on scatter degree

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7th international conference on machine learning and Cybernetics

作者： Xu, Jun-Ling Xu, Bao-Wen Wang, Cong Cui, Zi-Feng SE Univ Sch Comp Sci & Engn Nanjing 211189 Peoples R China Ricoh Imaging Technol Shanghai Co Ltd Shanghai 200233 Peoples R China

ISBN: (纸本)9781424420957

Feature selection is an important task in machine learning, pattern recognition and data mining. this paper proposed a new feature selection method for classification, named SD, which is based on scatter matrix used in linear discriminant analysis. the main feature of SD is its simplicity and independency of learning algorithms. High-dimensional data samples are first projected into a lower dimensional subspace of the original feature space by means of a linear transformation matrix, which can be attained according to the scatter degree of each feature, and then the scatter degree is used to measure the importance of each feature. A comparison of SD and some popular feature selection methods (information gain and X-2-test) is conducted, and the results of experiment carried out on 19 data sets show the advantages of SD.

关键词： feature selection scatter degree data mining

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