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Assisting data mining through Automated Planning

6th international conference on machine learning and data mining in pattern recognition

作者： Fernandez, Fernando Borrajo, Daniel Fernandez, Susana Manzano, David Univ Carlos III Madrid Leganes Spain Ericsson Espana Madrid Spain

ISBN: (纸本)9783642030697

the induction of knowledge from a data set. relies ill the execution of multiple data mining actions: to apply filters to clean and select the data, to train different algorithms (clustering, classification, regression, association), to evaluate the results using different approaches (cross validation, statistical analysis), to visualize the, results, etc. In a real data mining process, previous actions are executed several times, sometimes in a loop, until an accurate result is obtained. However, performing previous tasks require's a data mining engineer or expert which supervises the design and evaluate the whole process. the goat of this paper is to describe MOLE, an architecture to automatize the data, mining process. the architecture assumes than die data mining process can be seen from a Classical planning perspective! and hence. that classical planning tools can be used to design process. MOLE is built and instantiated oil the basis of i) standard languages to describe the data set and the data mining process, ii) available Cools to design, execute and evaluate the data mining processes.

关键词： data mining

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A granular computing framework for self-organizing maps

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NEUROCOMPUTING 2009年第13-15期72卷 2865-2872页

作者： Herbert, Joseph P. Yao, JingTao Univ Regina Dept Comp Sci Regina SK S4S 4P7 Canada

When using granular computing for problem solving, one can focus on a specific level of understanding without looking at unwanted details of subsequent (more precise) levels. We present a granular computing framework for growing hierarchical self-organizing maps. this approach is ideal since the maps are arranged in a hierarchical manner and each is a complete abstraction of a pattern within data. the framework allows us to precisely define the connections between map levels. Formulating a neuron as a granule, the actions of granule construction and decomposition correspond to the growth and absorption of neurons in the previous model. In addition, we investigate the effects of updating granules with new information on both coarser and finer granules that have a derived relationship. Called bidirectional update propagation, the method ensures pattern consistency among data abstractions. An algorithm for the construction, decomposition, and updating of the granule-based self-organizing map is introduced. With examples, we demonstrate the effectiveness of this framework for abstracting patterns on many levels. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Granular computing Self-organizing maps machine learning

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Sequential Hierarchical pattern Clustering

Sequential Hierarchical Pattern Clustering

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4th international conference pattern recognition in Bioinformatics

作者： Farran, Bassam Ramanan, Amirthalingam Niranjan, Mahesan Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England

ISBN: (纸本)9783642040306

Clustering is a, widely used unsupervised data analysis technique in machine learning. However, a common requirement amongst many existing clustering methods is that all pairwise distances between patterns must be computed in advance. this makes it computationallly expensive and difficult to cope with large scale data used in several applications, such as in bioinformatics. In this paper we propose a novel sequential hierarchical clustering technique that initially builds a hierarchical tree from a small fraction of the entire data, while the remaining data is processed sequentially and the tree adapted constructively. Preliminary results using this approach show that the quality of the clusters obtained does not degrade while reducing the computational needs.

关键词： On-line clustering Hierarchical clustering Large scale data Gene expression

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Ensemble learning: A Study on Different Variants of the Dynamic Selection Approach

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6th international conference on machine learning and data mining in pattern recognition

作者： Mendes-Moreira, Joao Jorge, Alipio Mario Soares, Carlos de Sousa, Jorge Freire Univ Porto Fac Engn DEI Oporto Portugal LIAAD INESC Porto LA Portugal Univ Porto Fac Ciencias Porto Portugal Univ Porto Fac Econ Porto Portugal Univ Porto Fac Engenharia DEIG Porto Portugal

ISBN: (纸本)9783642030697

Integration methods for ensemble learning can use two different approaches: combination or selection. the combination approach (also called fusion) consists on the combination of the predictions obtained by different models in the ensemble to obtain the final ensemble predication. the selection approach selects one (or more) models from the ensemble according to the prediction performance of these models on similar data from the validation set. Usually, the method to select similar data is the k-nearest neighbors with the Euclidean distance. In this paper we discuss other approaches to obtain similar data for the regression problem. We show that using similarity measures according to the target values improves results. We also show that selecting dynamically several models for the prediction task increases prediction accuracy comparing to the selection of just one model.

关键词： Forecasting

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GSML: A Unified Framework for Sparse Metric learning

GSML: A Unified Framework for Sparse Metric Learning

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9th IEEE international conference on data mining

作者： Huang, Kaizhu Ying, Yiming Campbell, Colin Chinese Acad Sci Natl Lab Pattern Recognit Inst Automat Beijing 100190 Peoples R China Univ Bristol Dept Math Engn Bristol BS8 1TR Avon England

ISBN: (纸本)9781424452422

there has been significant recent interest in sparse metric learning (SML) in which we simultaneously learn both a good distance metric and a low-dimensional representation. Unfortunately, the performance of existing sparse metric learning approaches is usually limited because the authors assumed certain problem relaxations or they target the SML objective indirectly. In this paper, we propose a Generalized Sparse Metric learning method (GSML). this novel framework offers a unified view for understanding many of the popular sparse metric learning algorithms including the Sparse Metric learning framework proposed in [15], the Large Margin Nearest Neighbor (LMNN) [21][22], and the D-ranking Vector machine (D-ranking VM) [14]. Moreoven GSML also establishes a close relationship with the Pairwise Support Vector machine [20]. Furthermore, the proposed framework is capable of extending many current non-sparse metric learning models such as Relevant Vector machine (RCA) [4] and a state-of-the-art method proposed in [23] into their sparse versions. We present the detailed framework, provide theoretical justifications, build various connections with other models, and propose a practical iterative optimization method, making the framework both theoretically important and practically scalable for medium or large datasets. A series of experiments show that the proposed approach can outperform previous methods in terms of both test accuracy and dimension reduction, on six realworld benchmark datasets.

关键词： learning algorithms

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Concept Drifting Detection on Noisy Streaming data in Random Ensemble Decision Trees

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6th international conference on machine learning and data mining in pattern recognition

作者： Li, Peipei Hu, Xuegang Liang, Qianghui Gao, Yunjun Hefei Univ China Sch Comp Sci & Informat Technol Hefei 230009 Peoples R China Singapore Management Univ Sch Informat Syst Singapore 178902 Singapore Zhejiang Univ Coll Comp Sci Hangzhou 310027 Peoples R China

ISBN: (纸本)9783642030697

Although a vast majority of inductive learning algorithms has been developed for handling of the concept drifting data streams, especially the ones in Wine of ensemble classification models, few of them could adapt to Hie detection oil the different types of concept drifts from noisy streaming data in a demand on overheads of time and space. Motivated by this, a new classification algorithm for Concept drifting Detection based on an ensembling model of Random Decision Trees (called CDRDT) is proposed in this paper. Extensive studies with synthetic and real streaming dam demonstrate that in comparison to several classification algorithms for concept drifting data streams, CDRDT not only could effectively and efficiently detect the potential concept changes in the noisy data streams, but also performs much better oil the abilities of runtime and space with an improvement in predictive accuracy. thus, our proposed algorithm provides a significant reference to the classification for concept drifting data streams with noise in a light, weight way.

关键词： data Streams Ensemble Decision Trees Concept Drift Noise

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Applying learning algorithms to music generation

Applying learning algorithms to music generation

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4th Indian international conference on Artificial Intelligence, IICAI 2009

作者： Lichtenwalter, Ryan N. Lichtenwalter, Katerina Chawla, Nitesh V. University of Notre Dame Notre Dame IN 46556 United States

ISBN: (纸本)9780972741279

there exist several music composition systems that generate blues chord progressions, jazz improvisation, or classical pieces. Such systems often work by applying a set of rules explicitly provided to the system to determine what sequence of output values is appropriate. Others use pattern recognition and generation techniques such as Markov models. these systems often suffer from mediocre performance and limited generality. We propose a system that goes from raw musical data to feature vector representation to classification models. We employ sliding window sequential machine learning techniques to generate classifiers that correspond to a training set of musical data. Our approach has the advantage of greater generality than explicitly specifying rules to a system and the potential to apply a wide variety of powerful existing non-sequential learning algorithms. We present the design and implementation of the composition system. We demonstrate the efficacy of the method, show and analyze successful samples of its output, and discuss ways in which it might be improved. Copyright © 2009 by IICAI.

关键词： Markov processes

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Aligning Bayesian Network Classifiers with Medical Contexts

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6th international conference on machine learning and data mining in pattern recognition

作者： van der Gaag, Linda C. Renooij, Silja Feelders, Ad de Groote, Arend Eijkemans, Marinus J. C. Broekmans, Frank J. Fauser, Bart C. J. M. Univ Utrecht Dept Informat & Comp Sci POB 80-089 NL-3508 TB Utrecht Netherlands Utrecht Med Ctr Dept Reprod Med & Gynaecol Utrecht Netherlands Univ Med Ctr Dept Publ Hlth NL-3000 CA Rotterdam Netherlands

ISBN: (纸本)9783642030697

While for many problems in medicine classification models are being developed, Bayesian network classifiers do not seem to have become is widely accepted within the medical community as logistic regression models. We compare first-order logistic regression and naive Bayesian classification in the domain of reproductive medicine and demonstrate that the two techniques can result in models of comparable performance. For Bayesian network classifiers to become more widely accepted within the medical community, we feel that they should be better aligned with their context of application. We describe how to incorporate well-known concepts of clinical relevance in the process Of Constructing and evaluating Bayesian network classifiers to achieve Such an alignment.

关键词： learning Bayesian network classifiers logistic regression medical alignment

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Effective Page Recommendation Algorithms Based on Distributed learning Automata

Effective Page Recommendation Algorithms Based on Distribute...

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4th international Multi-conference on Computing in the Global Information Technology

作者： Forsati, Rana Rahbar, Afsaneh Mahdavi, Mehrdad Islamic Azad Univ Dept Comp Engn Karaj Branch Karaj Iran Islamic Azad Univ Dept Comp Engn North Tehran Branch Tehran Iran Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9781424446803

Different efforts have been done to address the problem of information overload on the Internet. Recommender systems aim at directing users through this information space, toward the resources that best meet their needs and interests by extracting knowledge from the previous users' interactions. In this paper, we propose an algorithm to solve the web page recommendation problem. In our algorithm, we use distributed learning automata to learn the behavior of previous users' and recommend pages to the current user based on learned pattern. Our experiments on real data set show that the proposed algorithm performs better than the other algorithms that we compared to and, at the same time, it is less complex than other algorithms with respect to memory usage and computational cost too.

关键词： Personalization machine learning learning Automata Web mining

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Cross-Platform Analysis with Binarized Gene Expression data

Cross-Platform Analysis with Binarized Gene Expression Data

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4th international conference pattern recognition in Bioinformatics

作者： Tuna, Salih Niranjan, Mahesan Univ Southampton Sch Elect & Comp Sci ISIS Res Grp Southampton SO9 5NH Hants England

ISBN: (纸本)9783642040306

With widespread use of microarray technology as a potential diagnostics tool, the comparison of results obtained from the use of different platforms is of interest. When inference methods are designed using data collected using a particular platform, they are unlikely to work directly on measurements taken from a different type of array. We report on this cross-platform transfer problem, and show that, working with transcriptome representations at binary numerical precision, similar to the gene expression bar code method, helps circumvent the variability across platforms in several cancer classification tasks. We compare our approach with a recent machine learning method specifically designed for shifting distributions, i.e., problems in which the training and testing data are not, drawn from identical probability distributions, and show superior performance in three of the four problems in which we could directly compare.

关键词： Cross-platform analysis binary gene expression classification

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