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the application of machine-learning on lower limb motion analysis in human exoskeleton system 1

引用

4th international conference on Social Robotics, ICSR 2012

作者： Zhao, Cao-Yuan Zhang, Xiang-Gang Guo, Qing School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu 61731 China

ISBN: (数字)9783642341038

ISBN: (纸本)9783642341021

this article briefly describes the research status of the human exoskeleton system, and gives a summary of the human lower limb gait analysis. On this basis, the machine learning classification algorithms and clustering algorithms are used for offline data mining on a data collection of prototype, in order to establish a classifier for movement prediction and judgment of lower limb. At the same time, the article analyses the procedure of standing from sitting utilizing clustering algorithm for rehabilitation exoskeleton. Experiments mainly refer to the C4.5 decision tree algorithm, Bayesian classification algorithm and clustering algorithm EM. Weka software simulation results show that the lower limb motion can be judged by the classifier making use of gait analysis data accurately, and the lower limb motion for different users at different time and different environments could be clustered by cluster. Effectively using of these results can offer great convenience to the flexibility control for exoskeleton so that exoskeleton system could achieve human-computer coupling. © 2012 Springer-Verlag.

关键词： Clustering algorithms

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General Bayesian Network in Performing Micro-reality mining with Mobile Phone Usage data for Device Personalization

General Bayesian Network in Performing Micro-reality Mining ...

引用

4th international Mega-conference on Future Generation Information Technology (FGIT 2012)

作者： Chae, Seong Wook Hwang, Jungsik Lee, Kun Chang Sungkyunkwan Univ Seoul 110745 South Korea Sungkyunkwan Univ SKK Business Sch Seoul 110745 South Korea Sungkyunkwan Univ Dept Interact Sci Seoul 110745 South Korea

ISBN: (纸本)9783642355202

Personalization is an emerging issue in the digital age, where users have to deal with many kinds of digital devices and techniques. Moreover, the complexities of digital devices and their functions tend to increase rapidly, requiring careful attention to the questions of how to increase user satisfaction and develop more innovative digital products and services. To this end, we propose a new concept of micro-reality mining in which users' micro behaviors, revealed through their daily usage of digital devices and technologies, are scrutinized before key findings from the mining are embedded into new products and services. this paper proposes micro-reality mining for device personalization and examines the possibility of adopting a GBN (general Bayesian network) as a means of determining users' useful behavior patterns when using cell phones. through comparative experiments with other mining techniques such as SVM (support vector machine), DT (decision tree), NN (neural network), and other BN (Bayesian network) methods, we found that the GBN has great potential for performing micro-reality mining and revealing significant findings.

关键词： Reality mining Personalization General Bayesian Network Mobile phone Usage pattern

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Drift Detection and Model Selection Algorithms: Concept and Experimental Evaluation

引用

7th international conference on Hybrid Artificial Intelligent Systems (HAIS)

作者： Cal, Piotr Wozniak, Michal Wroclaw Univ Technol Dept Syst & Comp Networks PL-50370 Wroclaw Poland

ISBN: (纸本)9783642289309;9783642289316

the traditional classifier cannot keep its quality, when the concept drift appears. the paper proposes how to protect against classification quality decreasing when concept drift occurs. Invented methods do not train classifiers all the time but they try to use earlier gained knowledge about models and switched older model to suitable new one. In this work we assume that the set of models is known and stored as the pool of classifiers. then, by using drift detecting and searching models methods, we can choose the best model. Our propositions and the main characteristics of them were evaluated on the basis of the experiments which were carried out on chosen artificial data set.

关键词： machine learning supervised learning concept drift pattern recognition

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Outcome prediction for patients with severe traumatic brain injury using permutation entropy analysis of electronic vital signs data 1

引用

8th international conference on machine learning and data mining in pattern recognition, MLDM 2012

作者： Kalpakis, Konstantinos Yang, Shiming Hu, Peter F. MacKenzie, Colin F. Stansbury, Lynn G. Stein, Deborah M. Scalea, thomas M. Department of Computer Science and Electrical Engineering University of Maryland Baltimore County 1000 Hilltop Circle Baltimore MD 21250 United States R. Adams Cowley Shock Trauma Center Shock Trauma and Anesthesiology Research Center University of Maryland School of Medicine Baltimore MD 21201 United States

ISBN: (数字)9783642315374

ISBN: (纸本)9783642315367

Permutation entropy is computationally efficient, robust to noise, and effective to measure complexity. We used this technique to quantify the complexity of continuous vital signs recorded from patients with traumatic brain injury (TBI). Using permutation entropy calculated from early vital signs (initial 10~20% of patient hospital stay time), we built classifiers to predict in-hospital mortality, and mobility measured by 3-month Extended Glasgow Outcome Score (GOSE). Sixty patients with severe TBI produced a skewed dataset that we evaluated for accuracy, sensitivity and specificity. With early vital signs data, the overall prediction accuracy achieved 91.67% for mortality, and 76.67% for 3-month GOSE in testing datasets, using the leave-one-out cross validation. We also applied Receiver Operating Characteristic analysis to compare classifiers built from different learning methods. those results support the applicability of permutation entropy in analyzing the dynamic behavior of biomedical time series for early prediction of mortality and long-term patient outcomes. © 2012 Springer-Verlag.

关键词： Hospitals

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An attribute reduction method based on SVM

An attribute reduction method based on SVM

引用

international conference on Natural Computation (ICNC)

作者： Xiao Tang Zhiwen Mo School of Mathematical Sciences University of Electronic Science and Technology Chengdu China College of Mathematics and Software Science Sichuan Normal University Chengdu China

Recently, support vector machine (SVM), which is based on statistical learning theory emerges as a hot spot in artificial intelligence. It has been widely applied in pattern recognition and many fields. A new attribute reduction method based on SVM is proposed and realized. this paper provides a new way of thinking for attribute reduction.

关键词： Support vector machines Rough sets Approximation methods Educational institutions data mining Classification algorithms

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An Experimental Design to Evaluate Class Imbalance Treatment Methods

An Experimental Design to Evaluate Class Imbalance Treatment...

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international conference on machine learning and Applications (ICMLA)

作者： Gustavo Enrique De Almeida Prado Alves Batista Diego Furtado Silva Ronaldo Cristiano Prati

In the last decade, class imbalance has attracted a huge amount of attention from researchers and practitioners. Class imbalance is ubiquitous in machine learning, data mining and pattern recognition applications; therefore, these research communities have responded to such interest with literally dozens of methods and techniques. Surprisingly, there are still many fundamental open-ended questions such as "Are all learning paradigms equally affected by class imbalance?", "What is the expected performance loss for different imbalance degrees?" and "How much of the performance losses can be recovered by the treatment methods?". In this paper, we propose a simple experimental design to assess the performance of class imbalance treatment methods. this experimental setup uses real data sets with artificially modified class distributions to evaluate classifiers in a wide range of class imbalance. We employ such experimental design in a large-scale experimental evaluation with twenty-two data sets and seven learning algorithms from different paradigms. Our results indicate that the expected performance loss, as a percentage of the performance obtained with the balanced distribution, is quite modest (below 5%) for the most balanced distributions up to 10% of minority examples. However, the loss tends to increase quickly for higher degrees of class imbalance, reaching 20% for 1% of minority class examples. Support Vector machine is the classifier paradigm that is less affected by class imbalance, being almost insensitive to all but the most imbalanced distributions. Finally, we show that the sampling algorithms only partially recover the performance losses. On average, typically about 30\% or less of the performance that was lost due to class imbalance was recovered by random over-sampling and SMOTE.

关键词： Training Support vector machines Loss measurement learning systems Sampling methods machine learning Handwriting recognition

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the Equivalence of 2DLPP to LPP and (2D)²LPP for Face recognition

The Equivalence of 2DLPP to LPP and (2D)<SUP>2</SUP>LPP for ...

引用

4th international conference on machine Vision (ICMV) - Computer Vision and Image Analysis - pattern recognition and Basic Technologies

作者： Yang Jun Liu Yanli Sichuan Normal Univ Coll Comp Sci Chengdu 610066 Peoples R China Sichuan Univ Coll Comp Chengdu Sichuan Peoples R China Sichuan Normal Univ Coll Math & Software Sci Chengdu Sichuan Peoples R China

ISBN: (纸本)9780819490261

2DLPP is a valid dimensions reduction method which directly extracts feature from image matrix and can detect the intrinsic manifold structure of data by preserving the local information of training data. We analyze the relation between 2DLPP and LPP. We demonstrate they are equivalent on some special conditions. Conventional 2DLPP is working in the row direction of images. We proposed an alternative 2DLPP which is working in the column direction of images. By simultaneously considering the row and column directions, we develop the two-directional 2DLPP, i.e. (2D)(LPP)-L-2. the proposed method not only extracts feature with lower dimension than 2DLPP, but also take full advantage of row and column structure information of images. Experiment results on two standard face databases demonstrate the effectiveness of the proposed method.

关键词： Locality Preserving Projections (LPP) two dimension LPP (2D)(LPP)-L-2 manifold learning face recognition

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the WAMS Power data Processing based on Hadoop

The WAMS Power Data Processing based on Hadoop

引用

the 2012 4th international conference on machine learning and Computing（ICMLC 2012）

作者： Zhaoyang Qu 1 ,Shilin Zhang 2 1 School of Information Engineering,Northeast Dianli University,Jilin Jilin 132012,China 2 School of Information Engineering,Northeast Dianli University,Jilin Jilin 132012,China

For the mass data efficient processing power,Cloud computing platforms started to popularize in the world scope,which is mainly used to mass data processing and analysis,and it's better to save and use hardware *** massive WAMS data,this paper used the MapReduce to make parallel data ETL operations for several files,used MapReduce to to improve Apriori algorithm for improve the efficiency of data mining,and proposed the model of data mining of text log file based on *** to this model and created the platform for mining of cascading failure power site based on Hadoop,Which digged out the relationship of power site when cascading failures occurred,and verify the efficiency of data mining on *** platform is suitable for mass power grid files data mining by high performance local area network connection of computer cluster.

关键词： Cloud computing data mining WAMS data MapReduce ETL

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Predictability score-based feature selection

Predictability score-based feature selection

引用

international conference on System theory, Control, and Computing (ICSTCC)

作者： Marian V. Crăciun Adina Cocu Luminita Dumitriu Department of Computer and Information Technology Dunãrea de Jos Galati University Romania

the greater than ever amount of data used in predictive data mining calls for new and flexible approaches, based on soft computing methods, with the purpose of recognizing the valuable attributes in datasets. the selection of relevant features is an important part of the preprocessing step in pattern recognition, statistics, knowledge discovery and data mining. Discovering attributes with little or without predictive information allows overfitting avoidance and comprehensibility improvement of the resulting model. In the literature, there are several measures to estimate the quality of the attributes. this paper proposes a predictability-based feature selection technique, in concert with a fuzzy inference system, to point out the potential variables in predictive modeling. the same as Relief family methods, this approach takes into account the context of other attributes, given the target value, to assess the predictability of attributes according to how well their values distinguish between instances that are near each other. Using Bayesian decision theory and fuzzy rules, the system provides, for each feature, its relevancy, as qualitative information and its predictability score as quantitative measure. Encouraging first results presented in two case studies conclude this paper, bringing experimental evidence to support our proposal. However, theoretical and experimental investigations must continue until better feature selection strategies will emerge. Our work is just another step toward this goal.

关键词： Fuzzy logic Iris machine learning Predictive models Training Context Filtering theory

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pattern recognition and machine Intelligence 2011

引用

丛书名： Lecture Notes in Computer Science

2011年

作者： Sergei O. Kuznetsov Deba P. Mandal Malay K. Kundu Sankar Kumar Pal

ISBN: (数字)9783642217869

ISBN: (纸本)9783642217852

this book constitutes the refereed proceedings of the 4th international conference on pattern recognition and machine Intelligence, PReMI 2011, held in Moscow, Russia in June/July 2011. the 65 revised papers presented together with 5 invited talks were carefully reviewed and selected from 140 submissions. the papers are organized in topical sections on pattern recognition and machine learning; image analysis; image and video information retrieval; natural language processing and text and data mining; watermarking, steganography and biometrics; soft computing and applications; clustering and network analysis; bio and chemo analysis; and document image processing.

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