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distributed ensemble Feature Selection Framework for High-Dimensional and High-Skewed Imbalanced Big Dataset

Distributed Ensemble Feature Selection Framework for High-Di...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Soheili, Majid Haeri, Maryam Amir Amir Islamic Azad Univ Comp Engn Dept Neka Branch Neka Iran Univ Twente Learning Data Analyt & Technol Dept Enschede Netherlands

ISBN: (纸本)9781728190488

The class-imbalance problem emerges when the class labels of a dataset have a skewed distribution. In this circumstance, the instances belonging to one class, which is exactly the principal purpose, are dominated thoroughly by the instances belonging to other classes. In recent years, feature selection for high-dimensional imbalanced data has become attraction research scope. This technique concerns selecting an informative feature set to improve the accuracy of the classification model. Moreover, as a subcategory of feature selection, the feature ranking technique has been deliberated to cope with high-dimensional datasets in the last decade. On the one hand, most traditional feature selection methods are not scalable, which is critical to cope with large-scale datasets. On the other hand, scalability is an intrinsic characteristic of the ensemble learning approach. This paper proposes a distributed ensemble Imbalanced feature selection framework, called DEIM, to deal with big imbalanced datasets. The DEIM, at first, transforms default data partitions to representative partitions in a single pass. Second, it applies a feature ranking method in a bagging approach upon each partition independently. Finally, It fuses intermediate feature rankings in a stacking strategy. In this paper, two traditional feature ranking algorithms, ReliefF and QPFS, are plugged into DEIM. Therefore, two methods DEIM-Relief and DEIM-QPFS, are produced. Experiments are accomplished on three big imbalanced datasets and upon a computer cluster. The empirical study depicts that the produced methods are scalable. Also, they have lower execution times, and their final results can induce better classification models than DiReliefF and DQPFS.

关键词： Scalable Feature Selection distributed ensemble learning Imbalanced Big Data Set

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GraphEL: A Graph-based ensemble learning Method for distributed Diagnostics and Prognostics in the Industrial Internet of Things 24

GraphEL: A Graph-based Ensemble Learning Method for Distribu...

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24th IEEE International Conference on Parallel and distributed Systems (ICPADS)

作者： Zhou, Chongyu Tham, Chen-Khong Natl Univ Singapore Dept Elect & Comp Engn Singapore Singapore

ISBN: (纸本)9781538673089

ensemble learning (EL) methods have been shown to be effective for diagnostics and prognostics in industrial systems. By combining the learning ability of different base learners, EL has the potential to reduce the total complexity of the learning system while solving a difficult problem satisfactorily. Recent advantages in Industrial Internet of Things (IIoT) and edge computing technologies have started a new paradigm of distributed diagnostics and prognostics. However, existing EL methods that mainly focus on a centralized setting cannot adapt to the edge computing scenario, which significantly constrains the application of these EL methods in real industrial environments. In this paper, we present a new approach to EL called Graph-based ensemble learning (GraphEL) to enable distributed diagnostics and prognostics in real industrial environments. Comparing with existing methods, the proposed GraphEL framework builds different base learners for different subsystems. Furthermore, a graphical model is constructed to define the correlation structures among the outputs of different base learners in the ensemble such that they can be collaboratively trained to optimize the learning performance of the ensemble. Via simple message passing, the proposed GraphEL framework can be executed in a fully distributed manner which is suitable for edge computing. The performance of the proposed GraphEL framework is evaluated using two real-world industrial data sets where we demonstrate the advantages of GraphEL compared to existing EL methods.

关键词： Industrial Internet of Things (IIoT) smart manufacturing diagnostics and prognostics distributed ensemble learning

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Concerto: Leveraging ensembles for Timely, Accurate Model Training Over Voluminous Datasets 7

Concerto: Leveraging Ensembles for Timely, Accurate Model Tr...

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7th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)

作者： Budgaga, Walid Malensek, Matthew Pallickara, Sangmi Lee Pallickara, Shrideep Colorado State Univ Dept Comp Sci Ft Collins CO 80523 USA Univ San Francisco San Francisco CA USA

ISBN: (纸本)9780738123967

As data volumes increase, there is a pressing need to make sense of the data in a timely fashion. Voluminous datasets are often multidimensional with individual data points representing a vector of features. Data scientists fit models to the data using all features or a subset thereof and then use these models to inform their understanding of phenomena or make predictions. The performance of these analytical models is assessed based on their accuracy and ability to generalize on unseen data. Several frameworks exist for drawing insights from voluminous datasets, but have limited scalability (which leads to prolonged training times), poor resource utilization, narrow applicability across problem domains, and insufficient support for combining diverse model fitting algorithms. In this study, we describe our methodology for scalable supervised learning over voluminous datasets. The methodology explores the effect of controlled partitioning of the feature space, as well as how analytical models can be combined to preserve accuracy. Rather than build a single, all-encompassing model, we enable practitioners to construct an ensemble of models that are trained independently in parallel over different portions of the data space. This can provide faster training times and increased prediction accuracy overall;our empirical benchmarks demonstrate the suitability of our approach using real-world data.

关键词： distributed ensemble learning voluminous datasets parallel model training

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