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pattern mining for routine behaviour discovery in pervasive healthcare environments

Pattern mining for routine behaviour discovery in pervasive ...

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5th Int conference on Information Technol and Applications in Biomedicine in Conjunction with the 2nd Int Symposium and Summer School on Biomedical and Health Engineering

作者： Ali, Raza ElHelw, Mohamed Atallah, Louis Lo, Benny Yang, Guang-Zhong Univ London Imperial Coll Sci Technol & Med Dept Comp London SW7 2AZ England

ISBN: (纸本)9781424422548

Pervasive sensing is set to transform the future of patient care by continuous and intelligent monitoring of patient well-being. In practice, the detection of patient activity patterns over different time resolutions can be a complicated procedure, entailing the utilisation of multi-tier software architectures and processing of large volumes of data. this paper describes a scalable, distributed software architecture that is suitable for managing continuous activity data streams generated from body sensor networks. A novel pattern mining algorithm is applied to pervasive sensing data to obtain a concise, variable-resolution representation of frequent activity patterns over time. the identification of such frequent patterns enables the observation of the inherent structure present in a patient's daily activity for analyzing routine behaviour and its deviations.

关键词： body sensor networks frequent pattern mining activity recognition behaviour profiling

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Cost-sensitive XCS Classifier System Addressing Imbalance Problems

Cost-sensitive XCS Classifier System Addressing Imbalance Pr...

引用

5th international conference on Fuzzy Systems and Knowledge Discovery

作者： thach, Nguyen Huy Rojanavasu, Porntep Pinngern, Ouen King Mongkuts Inst Technol Ladkrabang ReCCIT Fac Engn Dept Comp Engn Bangkok 10520 Thailand

ISBN: (纸本)9780769533056

the class imbalance problem has been recognized as a crucial problem in machine learning and data mining. learning systems tend to be biased towards the majority class and thus have poor generalization for the minority class instances. this paper analyses the imbalance problem in accuracy-based learning classifier systems. In particular, we propose a novel approach based on XCS classifier system and cost-sensitive learning. In our approach, the reward value of correctly identifying the positive (rare) class outweighs the value of correctly identifying the common class. this research provides guidelines to set reward base on the dataset imbalance ratio and a method to calculate reward online base on the information collected by XCS during training is also proposed. Experimental results on synthetic and real-life datasets show that, with appropriate reward settings, XCS is robust to class imbalances.

关键词： Classification learning Classifier System XCS cost-sensitive learning imbalance problem

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data indexing for video shot mining based on optical flow

Data indexing for video shot mining based on optical flow

引用

5th international conference on Visual Information Engineering, VIE 2008

作者： Chen, Ying Hu, WeiMing Wu, Ou Zeng, XiangLin Department of Basic Sciences Beijing Electronic Science and Technology Institute Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9780863419140

Summarizing and understanding video shot based on their contents is an important research topic in multimedia data mining. this paper presents an efficient algorithm based on optical flow field to mine the motion data contained in a given video shot. Two features called magnitude of motion pixel and direction of motion pixel are constructed respectively and adopted to split the video shot into some categories automatically. the corresponding indexical structure is extracted from each category and directly applied to motion- based queries. A test system has been developed to prove the validity of our algorithm. the experimental results show that the algorithm performs well and will play an important role in content-based video shot retrieval. ©2008 the Institution of Engineering and Technology.

关键词： data mining

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Shape learning with function-described graphs

引用

5th international conference on Image Analysis and recognition

作者： Sanroma, Gerard Serratosa, Francese Alquezar, Rene Departament d'Enginyeria Informàtica i Matemàtiques Universitat Rovira i Virgili Spain Departament de Llenguatges i Sistemes Informàtics Universitat Politècnica de Catalunya Spain

ISBN: (纸本)9783540698111

A new method for shape learning is presented in this paper. this method incorporates abilities from both statistical and structural pattern recognition approaches to shape analysis. It borrows from statistical pattern recognition the capability of modelling sets of point coordinates, and from structural pattern recognition the ability of dealing with highly irregular patterns, such as those generated by points missingness. To that end we use a novel adaptation of Procrustes analysis, designed by us to align sets of points with missing elements. We use this information to generate sets of attributed graphs (AGs). From each set of AGs we synthesize a function-described graph (FDG), which is a type of compact representation that has the capability of probabilistic modelling of both structural and attribute information. Multivariate normal probability density estimation is used in FDGs instead of the originally used histograms. Comparative results of classification performance are presented of structural vs. attributes + structural information.

关键词： intermittently present landmarks missing data shape analysis procrustes analysis attributed graph function-described graph

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Neighborhood Preprocessing SVM for Large-scale data Sets Classification

Neighborhood Preprocessing SVM for Large-scale Data Sets Cla...

引用

5th international conference on Fuzzy Systems and Knowledge Discovery

作者： Chen, Guangxi Xu, Jian Xiang, Xiaolin Guilin Univ Elect Technol Sch Math & Comp Sci Guilin 541004 Peoples R China AnHui Univ Finance & Econ Sch Stat & Appl Math Beng Bu 233030 Peoples R China Sichuan Univ Coll Business Management Chengdu 610041 Peoples R China

ISBN: (纸本)9780769533056

Support vector machine (SVM) has been a promising method for data mining and machine learning in recent years. However, the training complexity of SVM is highly dependent on the size of a data set. A preprocessing Support Vector machines (PSVM) method for large-scale data set classification is presented to speed up SVM training. By analyzing the neighbor classification feature for each sample in training data set, a decision criterion was built to keep or delete this sample from the original data set without losing the classification. the new method can provide an SVM with high quality samples. Experiments with random data and UCI databases show that SVM with our new preprocessing method retains the high quality of training data set and the classification accuracy very well.

关键词： Support vector machines

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pattern recognition by DTW and Series data mining in 3D Stratum Modelling and 3D Visualization

Pattern Recognition by DTW and Series Data Mining in 3D Stra...

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5th international conference on Fuzzy Systems and Knowledge Discovery

作者： Chen, Oiang Zhang, Chao Mo Zhang, Ying Fang Song, Wan Qing Shanghai Univ Engn Sci Ctr Comp Shanghai 201620 Peoples R China Yangtze Univ Minist Educ Key Lab Explorat Tchnol Oil & Gas Res Hubei 434100 Peoples R China

ISBN: (纸本)9780769533056

three-dimension(3D) modeling and visualization of stratum plays important role in seismic active fault detection,of course in GeoInformation science. Well-logging data of strata is taken as time series. Similarity measure of subsequence search is proposed based on dynamic time warping (DTW), Realizing time series match in different length of time series. the frequent pattern mining experiment is carried on to survey data of strata by multivariable combination analysis, We supply the stratum geophysics attribute by the depth (time) records sequence in the non-drill hole survey data's places using these frequent patterns, combining structure frame and mathematics geology interpolation technology, establish 3D geology model of the target area,and develop the underground geologic body 3D visualization software depending on visual studio. net and OpenGL graph packages, realize 3D visualization system.

关键词： dynamic time warping (DTW) three-Dimension (3D) Modeling and visualization stratum modelling 3D geology model

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A Survey on Statistical pattern Feature Extraction

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4th international conference on Intelligent Computing

作者： Ding, Shifei Jia, Weikuan Su, Chunyang Jin, Fengxiang Shi, Zhongzhi China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221008 Peoples R China Acad Sinica Key Lab Intelligent Informat Proc Inst Comop Technol Beijing 100080 Peoples R China Agr Univ Coll Plant Protection Tai An Shandong 271018 Peoples R China Shandong Univ Sci Technol Coll Geoinformation Sci Engn Qingdao 266510 Peoples R China

ISBN: (纸本)9783540859833

the goal of statistical pattern feature extraction (SPFE) is 'low loss dimension reduction'. As the key link of pattern recognition, dimension reduction has become the research hot spot and difficulty in the fields of pattern recognition, machine learning, data mining and so on. pattern feature extraction is one of the most challenging research fields and has attracted the attention from many scholars. this paper summarily introduces the basic principle of SPFE, and discusses the latest progress of SPFE from the aspects such as classical statistical theories and their modifications, kernel-based methods, wavelet analysis and its modifications, algorithms integration and so on. At last we discuss the development trend of SPFE.

关键词： Extraction

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Feature extraction for improving the support vector machine biomedical data classifier performance

Feature extraction for improving the support vector machine ...

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5th Int conference on Information Technol and Applications in Biomedicine in Conjunction with the 2nd Int Symposium and Summer School on Biomedical and Health Engineering

作者： Kostka, Pawel S. Tkacz, Ewaryst J. Silesian Tech Univ Inst Elect Gliwice Poland

ISBN: (纸本)9781424422548

A support vector machine (SVM) is a relatively novel classifier based on the statistical learning theory. To increase the performance of classification, presented study focuses on the mixed domain (time&frequency) feature extraction preliminary to SVM application. Time and frequency domain selected features and discrete fast wavelet transform coefficients parameters including energy and entropy measures were the component of new feature vector. SVM classifier structure were adjusted by the selection of optimal for analysed application its kernel functions:both polynomial and radial basis functions. System was positively verified on the set of clinically classified ECG signals for control and atrial fibrillation (AF) disease patients taken from MITBIH data base. the measures of specificity and sensitivity computed for the set of 20 AF and 20 patients from control group divided into learning and verifying subsets were used to evaluate presented pattern recognition structure. Different types of wavelet basic function for feature extraction stage were tested to find the best system structure. Obtained results showed, that the ability of generalization for enriched feature extraction (FE)-SVM based system increased, due to selectively choosing only the most representative features for analyzed AF detection problem.

关键词： Feature extraction

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data mining technologies inspired from visual principle 1

引用

5th IFIP international conference on Intelligent Information Processing

作者： Xu, Zongben Xi'an Jiaotong University China

ISBN: (数字)9780387876856

ISBN: (纸本)9780387876849

In this talk we review the recent work done by our group on data mining (DM) technologies deduced from simulating visual principle. through viewing a DM problem as a cognition problems and treading a data set as an image with each light point located at a datum position, we developed a series of high efficient algorithms for clustering, classification and regression via mimicking visual principles. In pattern recognition, human eyes seem to possess a singular aptitude to group objects and find important structure in an efficient way. thus, a DM algorithm simulating visual system may solve some basic problems in DM research. From this point of view, we proposed a new approach for data clustering by modeling the blurring effect of lateral retinal interconnections based on scale space theory. In this approach, as the data image blurs, smaller light blobs merge into large ones until the whole image becomes one light blob at a low enough level of resolution. By identifying each blob with a cluster, the blurring process then generates a family of clustering along the hierarchy. the proposed approach provides unique solutions to many long standing problems, such as the cluster validity and the sensitivity to initialization problems, in clustering. We extended such an approach to classification and regression problems, through combatively employing the Weber's law in physiology and the cell response classification facts. the resultant classification and regression algorithms are proven to be very efficient and solve the problems of model selection and applicability to huge size of data set in DM technologies. We finally applied the similar idea to the difficult parameter setting problem in support vector machine (SVM). Viewing the parameter setting problem as a recognition problem of choosing a visual scale at which the global and local structures of a data set can be preserved, and the difference between the two structures be maximized in the feature space, we derived a di

关键词： data mining

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Oblique Decision Trees Using Embedded Support Vector machines in Classifier Ensembles

Oblique Decision Trees Using Embedded Support Vector Machine...

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7th IEEE international conference on Cybernetic Intelligent Systems

作者： Menkovski, Vlado Christou, Ioannis T. Efremidis, Sofoklis Athens Informat Technol 19-5 KmMarkopoulou Ave Peania 19002 Greece INTRACOM SA Telecom Solut Peania 19002 Greece

ISBN: (纸本)9781424429141

Classifier ensembles have emerged in recent years as a promising research area for boosting pattern recognition systems' performance. We present a new base classifier that utilizes Oblique Decision Tree technology based on Support Vector machines for the construction or oblique (non-axis parallel) tests on the nodes of the decision tree inducted. We describe a number of heuristic techniques for enhancing the tree construction process by better estimation of the gain obtained by an oblique split at any tree node. We then show how embedding the new classifier in an ensemble of classifiers using the classical Hedge(beta) algorithm boosts performance of the system. Testing 10-fold cross validation on UCI machine learning repository data sets shows that the new hybrid classifiers outperforms on average by more than 2.1% both the WEKA implementation or C4.5 (J48) and the SMO implementation of SVM in WEKA. the application of the particular ensemble algorithm is an excellent fit for online-learning applications where one seeks to improve performance of self-healing dependable computing systems based on reconfiguration by gradually and adaptively learning what constitutes good system configurations.

关键词： Classification Decision Trees On-line learning Supervised learning

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