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Design and analysis of the WCCI 2010 active learning challenge

Design and analysis of the WCCI 2010 active learning challen...

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2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 international Joint conference on Neural Networks, IJCNN 2010

作者： Guyon, Isabelle Cawley, Gavin Dror, Gideon Lemaire, Vincent Clopinet 955 Creston Road Berkeley CA 94708 United States University of East Anglia United Kingdom Academic College of Tel-Aviv-Yaffo Israel Orange France

ISBN: (纸本)9781424469178

We organized a data mining challenge on "active learning" for IJCNN/WCCI 2010, addressing machine learning problems where labeling data is expensive, but large amounts of unlabeled data are available at low cost. Examples include handwriting and speech recognition, document classification, vision tasks, drug design using recombinant molecules and protein engineering. Such problems might be tackled from different angles: learning from unlabeled data or active learning. In the former case, the algorithms must satisfy themselves with the limited amount of labeled data and capitalize on the unlabeled data with semi-supervised learning methods. Several challenges have addressed this problem in the past. In the latter case, the algorithms may place a limited number of queries to get new sample labels. the goal in that case is to optimize the queries and the problem is referred to as active learning. While the problem of active learning is of great importance, organizing a challenge in that area is non trivial. this is the problem we have addressed, and we describe our approach in this paper. the "active learning" challenge is part of the WCCI 2010 competition program (http://***2010. org/competition-program). the website of the challenge remains open for submission of new methods beyond the termination of the challenge as a resource for students and researchers (http://***/al). © 2010 IEEE.

关键词： Supervised learning

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An unsupervised vegetation classification algorithm based immune

An unsupervised vegetation classification algorithm based im...

引用

2010 6th international conference on Natural Computation, ICNC'10

作者： Liang, Chunlin Chen, Yuefeng Hong, Yindie Peng, Lingxi School of Information Guangdong Ocean University Zhanjiang China School of Computer Science Guangzhou University Guangzhou China

ISBN: (纸本)9781424459612

A novel artificial immune-based algorithm in predicting forest cover types with cartographic variables, referred to as POOTAI, is presented. Firstly, the definition of immune cell, antibody, and antigen are given. then, the dynamic models of immune response, immune regulation and immune memory are evolved, and the corresponding equations are established. Finally, it is tested by the well-known forest cover types data set of UCI (University of California at Irvine) and compared with other known algorithms. POOTAI shows that the classification accuracy is increased to 90.17%, which is higher than other classification algorithms. It has some good features such as continuous learning, dynamic adjustment, characteristics memory, and etc. © 2010 IEEE.

关键词： pattern recognition

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6th international conference on Advanced data mining and Applications, ADMA 2010

6th International Conference on Advanced Data Mining and App...

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6th international conference on Advanced data mining and Applications, ADMA 2010

ISBN: (纸本)9783642173158

the proceedings contain 118 papers. the special focus in this conference is on Advanced data mining and Applications. the topics include: Nearest neighbour distance matrix classification;classification inductive rule learning with negated features;fast retrieval of time series using a multi-resolution filter with multiple reduced spaces;DHPTID-HYBRID algorithm: A hybrid algorithm for association rule mining;an improved rough clustering using discernibility based initial seed computation;fixing the threshold for effective detection of near duplicate web documents in web crawling;topic-constrained hierarchical clustering for document datasets;discretization of time series dataset using relative frequency and k-nearest neighbor approach;MSDBSCAN: Multi-density scale-independent clustering algorithm based on DBSCAN;web users access paths clustering based on possibilistic and fuzzy sets theory;an efficient algorithm for mining erasable itemsets;discord region based analysis to improve data utility of privately published time series;Deep web sources classifier based on DSOM-EACO clustering model;kernel based K-medoids for clustering data with uncertainty;frequent pattern mining using modified cp-tree for knowledge discovery;spatial neighborhood clustering based on data field;surrounding influenced k-nearest neighbors: A new distance based classifier;a centroid k-nearest neighbor method;mining spatial association rules with multi-relational approach;an unsupervised classification method of remote sensing images based on ant colony optimization algorithm;discriminative Markov logic network structure learning based on propositionalization and χ2-Test;a novel clustering algorithm based on gravity and cluster merging;evolution analysis of a mobile social network;distance distribution and average shortest path length estimation in real-world networks;Weigted-FP-tree based XML query pattern mining.

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6th international conference on Advanced data mining and Applications, ADMA 2010

6th International Conference on Advanced Data Mining and App...

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6th international conference on Advanced data mining and Applications, ADMA 2010

ISBN: (纸本)9783642173127

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Advanced data mining and Applications - 6th international conference, ADMA 2010, Proceedings

Advanced Data Mining and Applications - 6th International Co...

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6th international conference on Advanced data mining and Applications, ADMA 2010

ISBN: (纸本)3642173152

the proceedings contain 118 papers. the topics discussed include: cost sensitive classification in data mining;web users access paths clustering based on possibilistic and fuzzy sets theory;on probabilistic models for uncertain sequential pattern mining;nearest neighbour distance matrix classification;fast retrieval of time series using a multi-resolution filter with multiple reduced spaces;fixing the threshold for effective detection of near duplicate web documents in web crawling;topic-constrained hierarchical clustering for document datasets;MSDBSCAN: multi-density scale-independent clustering algorithm based on DBSCAN;CPLDP: an efficient large dataset processing system built on cloud platform;a refinement approach to handling model misfit in semi-supervised learning;adapt the mRMR criterion for unsupervised feature selection;construction cosine radial basic function neural networks based on artificial immune networks;and spatial filter selection with LASSO for EEG classification.

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Advanced data mining and Applications - 6th international conference, ADMA 2010, Proceedings

Advanced Data Mining and Applications - 6th International Co...

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6th international conference on Advanced data mining and Applications, ADMA 2010

ISBN: (纸本)3642173128

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A Perceptron-Like Linear Supervised Algorithm for Text Classification

A Perceptron-Like Linear Supervised Algorithm for Text Class...

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6th international conference on Advanced data mining and Applications (ADMA)

作者： Gkanogiannis, Anestis Kalamboukis, theodore Athens Univ Econ & Business Dept Informat Athens Greece

ISBN: (纸本)9783642173158

A fast and accurate linear supervised algorithm is presented which compares favorably to other state of the art algorithms over several real data collections on the problem of text categorization. Although it has been already presented in [6], no proof of its convergence is given. From the geometric intuition of the algorithm it is evident that it is not a Perceptron or a gradient descent algorithm thus an algebraic proof of its convergence is provided in the case of linearly separable classes. Additionally we present experimental results on many standard text classification datasets and artificially generated linearly separable datasets. the proposed algorithm is very simple to use and easy to implement and it can be used in any domain without any modification on the data or parameter estimation.

关键词： Perceprton machine learning Classification

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An evaluation of neural network ensembles and model selection for time series prediction

An evaluation of neural network ensembles and model selectio...

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2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 international Joint conference on Neural Networks, IJCNN 2010

作者： Barrow, Devon K. Crone, Sven F. Kourentzes, Nikolaos Department of Management Science Lancaster University Management School Lancaster LA1 4YX United Kingdom

ISBN: (纸本)9781424469178

Ensemble methods represent an approach to combine a set of models, each capable of solving a given task, but which together produce a composite global model whose accuracy and robustness exceeds that of the individual models. Ensembles of neural networks have traditionally been applied to machine learning and pattern recognition but more recently have been applied to forecasting of time series data. Several methods have been developed to produce neural network ensembles ranging from taking a simple average of individual model outputs to more complex methods such as bagging and boosting. Which ensemble method is best;what factors affect ensemble performance, under what data conditions are ensembles most useful and when is it beneficial to use ensembles over model selection are a few questions which remain unanswered. In this paper we present some initial findings using neural network ensembles based on the mean and median applied to forecast synthetic time series data. We vary factors such as the number of models included in the ensemble and how the models are selected, whether randomly or based on performance. We compare the performance of different ensembles to model selection and present the results. © 2010 IEEE.

关键词： pattern recognition

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5th IAPR international conference on pattern recognition in Bioinformatics, PRIB 2010

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5th IAPR international conference on pattern recognition in Bioinformatics, PRIB 2010

ISBN: (纸本)9783642160004

the proceedings contain 38 papers. the special focus in this conference is on pattern recognition in Bioinformatics. the topics include: An algorithm to find all identical motifs in multiple biological sequences;discovery of non-induced patterns from sequences;exploring homology using the concept of three-state entropy vector;A maximum-likelihood formulation and EM algorithm for the protein multiple alignment problem;polynomial supertree methods revisited;enhancing graph database indexing by suffix tree structure;Semi-supervised graph embedding scheme with active learning (SSGEAL): Classifying high dimensional biomedical data;iterated local search for biclustering of microarray data;machine learning study of DNA binding by transcription factors from the LacI family;Biologically-aware latent dirichlet allocation (BaLDA) for the classification of expression microarray;measuring the quality of shifting and scaling patterns in biclusters;frequent episode mining to support pattern analysis in developmental biology;Time series gene expression data classification via L1-norm temporal SVM;Sub-grid and spot detection in DNA microarray images using optimal multi-level thresholding;quantification of cytoskeletal protein localization from high-content images;pattern recognition for high throughput zebrafish imaging using genetic algorithm optimization;consensus of ambiguity: theory and application of active learning for biomedical image analysis;semi-supervised learning of sparse linear models in mass spectral imaging;A matrix algorithm for RNA secondary structure prediction;Joint loop end modeling improves covariance model based non-coding RNA gene search;exploiting long-range dependencies in protein β-sheet secondary structure prediction;alpha helix prediction based on evolutionary computation;an On/Off lattice approach to protein structure prediction from contact maps;preface.

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An autonomous incremental learning algorithm of Resource Allocating Network for online pattern recognition

An autonomous incremental learning algorithm of Resource All...

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2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 international Joint conference on Neural Networks, IJCNN 2010

作者： Ozawa, Seiichi Nakasaka, Sho Roy, Asim Graduate School of Engineering Kobe University Kobe 657-8501 Japan Department of Information Systems Arizona State University Tempe AZ 85048 United States

ISBN: (纸本)9781424469178

In this paper, we propose a new autonomous incremental learning algorithm for radial basis function networks called Autonomous learning algorithm for Resource Allocating Network (AL-RAN). the proposed AL-RAN can carried out the following operations autonomously: (1) data collection for initial learning, (2) data normalization, (3) allocation of RBFs, (4) setting and adjusting RBF widths, and (5) incremental learning. In this paper, we mainly improve the first four functions in the initial learning phase where a convergence criterion based on the class separability of collected data is adopted in order to reduce the computational costs. In AL-RAN, training data are first collected until the class separability is converged or the recognition accuracies for normalized and unnormalized data have a significant difference. then, an initial structure of ALRAN is autonomously determined from the collected data, and AL-RAN is trained with them. After the initial learning, the incremental learning of AL-RAN is conducted whenever a new training data is given. In the experiments, we evaluate ALRAN using five benchmark datasets. the experimental results demonstrate that the above autonomous functions work well and the number of collected data in the proposed AL-RAN is significantly decreased without sacrificing the final recognition accuracy as compared with the previous version of AL-RAN. © 2010 IEEE.

关键词： learning algorithms

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