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检索条件"任意字段=4th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2005"

共 135 条记录，以下是51-60 订阅

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mining ranking models from dynamic network data

Mining ranking models from dynamic network data

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

作者： Macchia, Lucrezia Ceci, Michelangelo Malerba, Donato Dipartimento di Informatica Università degli Studi di Bari via Orabona 4 70126 Bari Italy

ISBN: (纸本)9783642315367

In recent years, improvement in ubiquitous technologies and sensor networks have motivated the application of data mining techniques to network organized data. Network data describe entities represented by nodes, which may be connected with (related to) each other by edges. Many network datasets are characterized by a form of autocorrelation where the value of a variable at a given node depends on the values of variables at the nodes it is connected with. this phenomenon is a direct violation of the assumption that data are independently and identically distributed (i.i.d.). At the same time, it offers the unique opportunity to improve the performance of predictive models on network data, as inferences about one entity can be used to improve inferences about related entities. In this work, we propose a method for learning to rank from network data when data distribution may change over time. the learned models can be used to predict the ranking of nodes in the network for new time periods. the proposed method modifies the SVMRank algorithm in order to emphasize the importance of models learned in time periods during which data follow a data distribution that is similar to that observed in the new time period. We evaluate our approach on several real world problems of learning to rank from network data, coming from the area of sensor networks. © 2012 Springer-Verlag.

关键词： Sensor networks

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A pattern discovery model for effective text mining

A pattern discovery model for effective text mining

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

作者： Pipanmaekaporn, Luepol Li, Yuefeng School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane QLD Australia

ISBN: (纸本)9783642315367

the quality of extracted features is the key issue to text mining due to the large number of terms, phrases, and noise. Most existing text mining methods are based on term-based approaches which extract terms from a training set for describing relevant information. However, the quality of the extracted terms in text documents may be not high because of lot of noise in text. For many years, some researchers make use of various phrases that have more semantics than single words to improve the relevance, but many experiments do not support the effective use of phrases since they have low frequency of occurrence, and include many redundant and noise phrases. In this paper, we propose a novel pattern discovery approach for text mining. this approach first discovers closed sequential patterns in text documents for identifying the most informative contents of the documents and then utilise the identified contents to extract useful features for text mining. We develop a novel fusion method based on Dempster-Shafer's evidential reasoning which allows to combine the pieces of document to discover the knowledge (features). To evaluate the proposed approach, we adopt the feature extraction method for information filtering (IF). the experimental results conducted on Reuters Corpus Volume 1 and TREC topics confirm that the proposed approach could achieve excellent performance. © 2012 Springer-Verlag.

关键词： Information filtering

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Clustering data stream by a sub-window approach using DCA

Clustering data stream by a sub-window approach using DCA

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

作者： Ta, Minh thuy Le thi, Hoai An Boudjeloud-Assala, Lydia UFR MIM University of Lorraine Ile du Saulcy 57045 Metz France

ISBN: (纸本)9783642315367

data stream is one emerging topic of data mining, it concerns many applications involving large and temporal data sets such as telephone records data, banking data, multimedia data,... For mining of such data, one crucial strategy is analysis of packet data. In this paper, we are interested in an exploratory analysis of strategies for clustering data stream based on a sub-window approach and an efficient clustering algorithm called DCA (Difference of Convex functions Algorithm). Our approach consists of separating the data on different sub-windows and then apply a DCA clustering algorithm on each sub-window. Two clustering strategies are investigated: global clustering (on the whole data set) and independent local clustering (i.e. clustering independently on each sub-window). Our aims are study: (1) the efficiency of the independent local clustering, and (2) the adequation of local clustering and global clustering based on the same DCA clustering algorithm. Comparative experiments with clustering data stream using K-Means, a standard clustering method, on different data sets are presented. © 2012 Springer-Verlag.

关键词： Clustering algorithms

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Multi-label image annotation based on neighbor pair correlation chain

Multi-label image annotation based on neighbor pair correlat...

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

作者： Jiang, Guang Liu, Xi Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100049 China Information Technology Laboratory Fujitsu Research and Development Center Co. Ltd. Beijing 100025 China

ISBN: (纸本)9783642315367

Image annotation plays an important role in content-based image understanding, various machine learning methods have been proposed to solve this problem. In this paper, label correlation is considered as an undirected bipartite graph, in which each label are correlated by some common hidden topics. As a result, given a label, random walk with restart on the graph supplies a most related label, repeating this precedure leads to a label chain, which keep each adjacent labels pair correlated as maximally as possible. We coordinate the labels chain with its respective classifier training on bottom feature, and guide a classifier chain to annotate an image. the experiment illustrates that our method outperform both the baseline and another popular method. © 2012 Springer-Verlag.

关键词： learning systems

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Investigating usage of text segmentation and inter-passage similarities to improve text document clustering

Investigating usage of text segmentation and inter-passage s...

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

作者： Paliwal, Shashank Pudi, Vikram Center for Data Engineering International Institute of Information Technology Hyderabad India

ISBN: (纸本)9783642315367

Measuring inter-document similarity is one of the most essential steps in text document clustering. Traditional methods rely on representing text documents using the simple Bag-of-Words (BOW) model. A document is an organized structure consisting of various text segments or passages. Such single term analysis of the text treats whole document as a single semantic unit and thus, ignores other semantic units like sentences, passages etc. In this paper, we attempt to take advantage of underlying subtopic structure of text documents and investigate whether clustering of text documents can be improved if text segments of two documents are utilized, while calculating similarity between them. We concentrate on examining effects of combining suggested inter-document similarities (based on inter-passage similarities) with traditional inter-document similarities following a simple approach for the same. Experimental results on standard data sets suggest improvement in clustering of text documents. © 2012 Springer-Verlag.

关键词： Cluster analysis

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A new learning structure heuristic of Bayesian Networks from data

A new learning structure heuristic of Bayesian Networks from...

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

作者： Bouhamed, Heni Masmoudi, Afif Lecroq, thierry Rebaï, Ahmed University of Rouen LITIS EA 4108 1 rue Thomas Becket 76821 Mont-Saint-Aignan Cedex France Department of Mathematics Faculty of Science of Sfax Soukra B.P 802 Sfax Tunisia Bioinformatics Unit Centre of Biotechnologie of Sfax 3018 Sfax Tunisia

ISBN: (纸本)9783642315367

Nowadays, Bayesian Networks (BNs) have constituted one of the most complete, self-sustained and coherent formalisms useful for knowledge acquisition, representation and application through computer systems. Yet, the learning of these BNs structures from data represents a problem classified at an NP-hard range of difficulty. As such, it has turned out to be the most exciting challenge in the learning machine area. In this context, the present work's major objective lies in setting up a further solution conceived to be a remedy for the intricate algorithmic complexity problems imposed during the learning of BN-structure through a massively-huge data backlog. Our present work has been constructed according to the following framework;on a first place, we are going to proceed by defining BNs and their related problems of structure-learning from data. We, then, go on to propose a novel heuristic designed to reduce the algorithmic complexity without engendering any loss of information. Ultimately, our conceived approach will be tested on a car diagnosis as well as on a Lymphography diagnosis data-bases, while our achieved results would be discussed, along with an exposition of our conducted work's interests as a closing step to this work. © 2012 Springer-Verlag.

关键词： Bayesian networks

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Hot deck methods for imputing missing data: the effects of limiting donor usage

Hot deck methods for imputing missing data: The effects of l...

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

作者： Joenssen, Dieter William Bankhofer, Udo Technische Universität Ilmenau Fachgebiet für Quantitative Methoden Ilmenau Germany

ISBN: (纸本)9783642315367

Missing data methods, within the data mining context, are limited in computational complexity due to large data amounts. Amongst the computationally simple yet effective imputation methods are the hot deck procedures. Hot deck methods impute missing values within a data matrix by using available values from the same matrix. the object, from which these available values are taken for imputation within another, is called the donor. the replication of values leads to the problem, that a single donor might be selected to accommodate multiple recipients. the inherent risk posed by this is that too many, or even all, missing values may be imputed with the values from a single donor. To mitigate this risk, some hot deck variants limit the amount of times any one donor may be selected for donating its values. this inevitably leads to the question under which conditions such a limitation is sensible. this study aims to answer this question though an extensive simulation. the results show rather clear differences between imputations by hot deck methods in which the donor limit was varied. In addition to these differences, influencing factors are identified that determine whether or not a donor limit is sensible. © 2012 Springer-Verlag.

关键词： data mining

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SOStream: Self organizing density-based clustering over data stream

SOStream: Self organizing density-based clustering over data...

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

作者： Isaksson, Charlie Dunham, Margaret H. Hahsler, Michael Department of Computer Science and Engineering Southern Methodist University Dallas TX United States

ISBN: (纸本)9783642315367

In this paper we propose a data stream clustering algorithm, called Self Organizing density based clustering over data Stream (SOStream). this algorithm has several novel features. Instead of using a fixed, user defined similarity threshold or a static grid, SOStream detects structure within fast evolving data streams by automatically adapting the threshold for density-based clustering. It also employs a novel cluster updating strategy which is inspired by competitive learning techniques developed for Self Organizing Maps (SOMs). In addition, SOStream has built-in online functionality to support advanced stream clustering operations including merging and fading. this makes SOStream completely online with no separate offline components. Experiments performed on KDD Cup'99 and artificial datasets indicate that SOStream is an effective and superior algorithm in creating clusters of higher purity while having lower space and time requirements compared to previous stream clustering algorithms. © 2012 Springer-Verlag.

关键词： Self organizing maps

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

Outcome prediction for patients with severe traumatic brain ...

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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: (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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Artificial Intelligence and Computational Intelligence 2012

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丛书名： Lecture Notes in Computer Science

2012年

作者： Jingsheng Lei Fu Lee Wang Hepu Deng Duoqian Miao

ISBN: (数字)9783642334788

ISBN: (纸本)9783642334771

this volume proceedings contains revised selected papers from the 4th international conference on Artificial Intelligence and Computational Intelligence, AICI 2012, held in Chengdu, China, in October 2012. the total of 163 high-quality papers presented were carefully reviewed and selected from 724 submissions. the papers are organized into topical sections on applications of artificial intelligence, applications of computational intelligence, data mining and knowledge discovery, evolution strategy, expert and decision support systems, fuzzy computation, information security, intelligent control, intelligent image processing, intelligent information fusion, intelligent signal processing, machine learning, neural computation, neural networks, particle swarm optimization, and pattern recognition.

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