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检索条件"任意字段=2014 IEEE Symposium on Computational Intelligence in Big Data, CIBD 2014"

共 248 条记录，以下是21-30 订阅

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On Ensemble Components Selection in data Streams Scenario with Reoccurring Concept-Drift

On Ensemble Components Selection in Data Streams Scenario wi...

引用

ieee symposium Series on computational intelligence (ieee SSCI)

作者： Duda, Piotr Jaworski, Maciej Rutkowski, Leszek Czestochowa Tech Univ Inst Computat Intelligence Czestochowa Poland

ISBN: (纸本)9781538627266

In this article we consider the problem of data stream classification with recurring concept-drift. The proposed method determines when to add or remove a component from an ensemble. The algorithm discussed in this article is an extension of the ASE (Automatically Sized Ensemble) algorithm which guarantees that, with the assumed probability, adding a new component increases the accuracy not only for the current data chunk, but also for the entire data stream. The new approach improves the adaptation of the ASE algorithm to non-stationary environments, particularly when the data stream is generated by different data distributions that appear and disappear alternately. The procedure uses a Kullback-Leibler discrepancy to measure the suitability of elements for the considered data distribution.

关键词： data stream classification ensemble methods reoccurring concept-drift Kullback-Leibler divergence

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On Applying the Restricted Boltzmann Machine to Active Concept Drift Detection

On Applying the Restricted Boltzmann Machine to Active Conce...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Jaworski, Maciej Duda, Piotr Rutkowski, Leszek Czestochowa Tech Univ Inst Computat Intelligence Czestochowa Poland

ISBN: (纸本)9781538627266

In this paper the issue of active concept drift detection in time-varying data stream mining is taken under consideration. The Restricted Boltzmann Machine (RBM) is proposed to be applied as a drift detector. The RBMs are neural networks which are able to learn generative models of data. After training they contain a compressed information about the distribution from which the training data were drawn. We suppose that such an RBM learned on a part of the data stream can then be used to determine whether the new data elements from the stream are drawn from the same distribution or not. Two indicators are proposed to be used for evaluation of incoming data: the free energy and the reconstruction error. Their computation is relatively fast, hence they are suitable for data stream scenario. Preliminary experimental results demonstrate that the proposed tool is able to deal with different types of concept drift, e.g. the sudden or the gradual.

关键词： data stream concept drift detection Restricted Boltzmann Machine generative models

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Classification of high dimensional data using LASSO ensembles

Classification of high dimensional data using LASSO ensemble...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Urda, Daniel Franco, Leonardo Jerez, Jose M. Univ Malaga ETSI Informat Malaga Spain MIUC Inst Invest Biomed Malaga IBIMA Marbella Spain Univ Malaga Andalucia Tech Dept Lenguajes & Ciencias Computac Espana Malaga Spain Inst Invest Biomed Malaga IBIMA Malaga Spain

ISBN: (纸本)9781538627266

The estimation of multivariable predictors with good performance in high dimensional settings is a crucial task in biomedical contexts. Usually, solutions based on the application of a single machine learning model are provided while the use of ensemble methods is often overlooked within this area despite the well-known benefits that these methods provide in terms of predictive performance. In this paper, four ensemble approaches are described using LASSO base learners to predict the vital status of a patient from RNA-Seq gene expression data. The results of the analysis carried out in a public breast invasive cancer (BRCA) dataset shows that the ensemble approaches outperform statistically significant the standard LASSO model considered as baseline case. We also perform an analysis of the computational costs involved for each of the approaches, providing different usage recommendations according to the available computational power.

关键词： Predictive models data models Biological system modeling computational modeling Standards Gene expression Analytical models

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Pipelining user trajectory analysis and visual process maps for habit mining

Pipelining user trajectory analysis and visual process maps ...

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2017 ieee SmartWorld Ubiquitous intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and big data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017

作者： Leotta, Francesco Mecella, Massimo Sora, Daniele Spinelli, Giovanni Sapienza Università di Roma Dipartimentó di Ingegneria Informatica Automatica e Gestionale Antonio Ruberti Italy

ISBN: (纸本)9781538604342

Models of human habits in smart spaces can be expressed by using a multitude of formalisms whose readability influences the possibility of being validated by human experts. In this paper we present a visual analysis pipeline that allows, starting from the sensor log of a smart space, to graphically visualize human habits. The intuition here is to apply techniques borrowed from the area of business process automation and mining on a version of the sensor log preprocessed in order to translate raw sensor measurements into human actions. The paper also presents some hints of how the proposed method can be employed to automatically extract models to be reused for ambient intelligence. © 2017 ieee.

关键词： Ambient intelligence

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Exact Fuzzy k-Nearest Neighbor Classification for big datasets

Exact Fuzzy k-Nearest Neighbor Classification for Big Datase...

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ieee International Conference on Fuzzy Systems (FUZZ-ieee)

作者： Maillo, Jesus Luengo, Julian Garcia, Salvador Herrera, Francisco Triguero, Isaac Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain Univ Nottingham Sch Comp Sci Jubilee Campus Nottingham NG8 1BB England

ISBN: (纸本)9781509060344

The k-Nearest Neighbors (kNN) classifier is one of the most effective methods in supervised learning problems. It classifies unseen cases comparing their similarity with the training data. Nevertheless, it gives to each labeled sample the same importance to classify. There are several approaches to enhance its precision, with the Fuzzy k-Nearest Neighbors (Fuzzy-kNN) classifier being among the most successful ones. Fuzzy-kNN computes a fuzzy degree of membership of each instance to the classes of the problem. As a result, it generates smoother borders between classes. Apart from the existing kNN approach to handle big datasets, there is not a fuzzy variant to manage that volume of data. Nevertheless, calculating this class membership adds an extra computational cost becoming even less scalable to tackle large datasets because of memory needs and high runtime. In this work, we present an exact and distributed approach to run the Fuzzy-kNN classifier on big datasets based on Spark, which provides the same precision than the original algorithm. It presents two separately stages. The first stage transforms the training set adding the class membership degrees. The second stage classifies with the kNN algorithm the test set using the class membership computed previously. In our experiments, we study the scaling-up capabilities of the proposed approach with datasets up to 11 million instances, showing promising results.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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ieee SSCI 2014 - 2014 ieee symposium Series on computational intelligence - CIDM 2014: 2014 ieee symposium on computational intelligence and data Mining, Proceedings

IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational...

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5th ieee symposium on computational intelligence and data Mining, CIDM 2014

ISBN: (纸本)9781479945191

The proceedings contain 67 papers. The topics discussed include: clustering data over time using kernel spectral clustering with memory;agglomerative hierarchical kernel spectral data clustering;quantum clustering - a novel method for text analysis;generalized information theoretic cluster validity indices for soft clusterings;a density-based clustering of the self-organizing map using graph cut;new bilinear formulation to semi-supervised classification based on kernel spectral clustering;convex multi-task relationship learning using hinge loss;precision-recall-optimization in learning vector quantization classifiers for improved medical classification systems;semi-supervised source extraction methodology for the nosological imaging of glioblastoma response to therapy;and relational data partitioning using evolutionary game theory.

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ieee SSCI 2014 - 2014 ieee symposium Series on computational intelligence - CICARE 2014: 2014 ieee symposium on computational intelligence in Healthcare and e-Health, Proceedings

IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational...

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2014 2nd ieee symposium on computational intelligence in Healthcare and e-Health, CICARE 2014

ISBN: (纸本)9781479945283

The proceedings contain 27 papers. The topics discussed include: a feasibility study of using a single kinect sensor for rehabilitation exercises monitoring: a rule based approach;automating assessment in video game teletherapy: data cutting;an efficient computer aided decision support system for breast cancer diagnosis using echo state network classifier;intelligent image processing techniques for cancer progression, detection, recognition and prediction in the human liver;an approximate inverse recipe method with application to automatic food analysis;the design, implementation and evaluation of a relaxation service with facial emotion detection;intelligent emotions stabilization system using standardized images, breath sensor and biofeedback - new concept;and cognitively inspired speech processing for multimodal hearing technology.

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ieee SSCI 2014 - 2014 ieee symposium Series on computational intelligence - MCDM 2014: 2014 ieee symposium on computational intelligence in Multi-Criteria Decision-Making, Proceedings

IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational...

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2014 ieee symposium on computational intelligence in Multi-Criteria Decision-Making, MCDM 2014

ISBN: (纸本)9781479944682

The proceedings contain 28 papers. The topics discussed include: Robustness Threshold Methodology for Multi-criteria based Ranking using Imprecise data;Generating Diverse and Accurate Classifier Ensembles Using Multi-Objective Optimization;Selection of Solutions in Multi-Objective Optimization: Decision Making and Robustness;A Multi-objective Genetic Algorithm based on NSGA /I for Deriving Final Ranking from a Medium-Sized Fuzzy Outranking Relation;Clustering Decision Makers with respect to similarity of views;Multi-Genomic Algorithms;A Perceptual Fuzzy Neural Model;Multi-criteria Approaches for Predictive Model Generation: A Comparative Experimental Study;Evaluation of E-commerce System Trustworthiness Using Multi-criteria Analysis;and Nonlinear Programming Models and Method for Interval- Valued Multi-objective Cooperative Games.

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ieee SSCI 2014 - 2014 ieee symposium Series on computational intelligence - CIBCI 2014: 2014 ieee symposium on computational intelligence in Brain Computer Interfaces, Proceedings

IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational...

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2014 ieee symposium on computational intelligence in Brain Computer Interfaces, CIBCI 2014

ISBN: (纸本)9781479945443

The proceedings contain 10 papers. The topics discussed include: development of an autonomous BCI wheelchair;across-subject estimation of 3-back task performance using EEG signals;abnormal event detection in EEG imaging - comparing predictive and model-based approaches;sensitivity analysis of Hilbert transform with band-pass FIR filters for robust brain computer interface;electroencephalographic method using fast Fourier transform overlap processing for recognition of right- or left-handed elbow flexion motor imagery;development of SSVEP-based BCI using common frequency pattern to enhance system performance;EEG-based golf putt outcome prediction using support vector machine;non-supervised technique to adapt spatial filters for ECoG data analysis;identification of three mental states using a motor imagery based brain machine interface;and EEG subspace analysis and classification using principal angles for brain-computer interfaces.

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Stable Model Semantics for Tuple-Generating Dependencies Revisited 17

Stable Model Semantics for Tuple-Generating Dependencies Rev...

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36th ACM SIGMOD-SIGACT-SIGAI symposium on Principles of database Systems (PODS)

作者： Alviano, Mario Morak, Michael Pieris, Andreas Univ Calabria Dept Math & CS Commenda Di Rende Italy TU Wien Inst Informat Syst Vienna Austria Univ Edinburgh Sch Informat Edinburgh Midlothian Scotland

ISBN: (纸本)9781450341981

Normal tuple-generating dependencies (NTGDs) are TGDs enriched with default negation, a.k.a. negation as failure. Query answering under NTGDs, where negation is interpreted according to the stable model semantics, is an intriguing new problem that gave rise to flourishing research activity in the database and KR communities. So far, all the existing works that investigate this problem, except for one recent paper that adopts an operational semantics based on the chase, follow the so-called logic programming (LP) approach. According to the LP approach, the existentially quantified variables are first eliminated via Skolemization, which leads to a normal logic program, and then the standard stable model semantics for normal logic programs is applied. However, as we discuss in the paper, Skolemization is not appropriate in the presence of default negation since it fails to capture the intended meaning of NTGDs, while the operational semantics mentioned above fails to overcome the limitations of the LP approach. This reveals the need to adopt an alternative approach to stable model semantics that is directly applicable to NTGDs with existentially quantified variables. We propose such an approach based on a recent characterization of stable models in terms of second-order logic, which indeed overcomes the limitations of the LP approach. We then perform an in-depth complexity analysis of query answering under prominent classes of NTGDs based on the main decidability paradigms for TGDs, namely weak-acyclicity, guardedness and stickiness. Interestingly, weakly-acyclic NTGDs give rise to robust and highly expressive query languages that allow us to solve in a declarative way problems in the second level of the polynomial hierarchy.

关键词： computational complexity

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