The Experts below are selected from a list of 53916 Experts worldwide ranked by ideXlab platform
Zijian Zheng - One of the best experts on this subject based on the ideXlab platform.
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Multistrategy Ensemble Learning: Reducing error by combining Ensemble Learning techniques
IEEE Transactions on Knowledge and Data Engineering, 2004Co-Authors: Geoffrey I. Webb, Zijian ZhengAbstract:Ensemble Learning strategies, especially boosting and bagging decision trees, have demonstrated impressive capacities to improve the prediction accuracy of base Learning algorithms. Further gains have been demonstrated by strategies that combine simple Ensemble formation approaches. We investigate the hypothesis that the improvement in accuracy of multistrategy approaches to Ensemble Learning is due to an increase in the diversity of Ensemble members that are formed. In addition, guided by this hypothesis, we develop three new multistrategy Ensemble Learning techniques. Experimental results in a wide variety of natural domains suggest that these multistrategy Ensemble Learning techniques are, on average, more accurate than their component Ensemble Learning techniques.
Geoffrey I. Webb - One of the best experts on this subject based on the ideXlab platform.
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Multistrategy Ensemble Learning: Reducing error by combining Ensemble Learning techniques
IEEE Transactions on Knowledge and Data Engineering, 2004Co-Authors: Geoffrey I. Webb, Zijian ZhengAbstract:Ensemble Learning strategies, especially boosting and bagging decision trees, have demonstrated impressive capacities to improve the prediction accuracy of base Learning algorithms. Further gains have been demonstrated by strategies that combine simple Ensemble formation approaches. We investigate the hypothesis that the improvement in accuracy of multistrategy approaches to Ensemble Learning is due to an increase in the diversity of Ensemble members that are formed. In addition, guided by this hypothesis, we develop three new multistrategy Ensemble Learning techniques. Experimental results in a wide variety of natural domains suggest that these multistrategy Ensemble Learning techniques are, on average, more accurate than their component Ensemble Learning techniques.
Danilo P. Mandic - One of the best experts on this subject based on the ideXlab platform.
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Tensor Ensemble Learning for Multidimensional Data.
arXiv: Signal Processing, 2018Co-Authors: Ilia Kisil, Ahmad Moniri, Danilo P. MandicAbstract:In big data applications, classical Ensemble Learning is typically infeasible on the raw input data and dimensionality reduction techniques are necessary. To this end, novel framework that generalises classic flat-view Ensemble Learning to multidimensional tensor-valued data is introduced. This is achieved by virtue of tensor decompositions, whereby the proposed method, referred to as tensor Ensemble Learning (TEL), decomposes every input data sample into multiple factors which allows for a flexibility in the choice of multiple Learning algorithms in order to improve test performance. The TEL framework is shown to naturally compress multidimensional data in order to take advantage of the inherent multi-way data structure and exploit the benefit of Ensemble Learning. The proposed framework is verified through the application of Higher Order Singular Value Decomposition (HOSVD) to the ETH-80 dataset and is shown to outperform the classical Ensemble Learning approach of bootstrap aggregating.
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GlobalSIP - TENSOR Ensemble Learning FOR MULTIDIMENSIONAL DATA
2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018Co-Authors: Ilia Kisil, Ahmad Moniri, Danilo P. MandicAbstract:In big data applications, classical Ensemble Learning is typically infeasible on the raw input data and dimensionality reduction techniques are necessary. To this end, novel framework that generalises classic flat-view Ensemble Learning to multidimensional tensor-valued data is introduced. This is achieved by virtue of tensor decompositions, whereby the proposed method, referred to as tensor Ensemble Learning (TEL), decomposes every input data sample into multiple factors which allows for a flexibility in the choice of multiple Learning algorithms in order to improve test performance. The TEL framework is shown to naturally compress multidimensional data in order to take advantage of the inherent multi-way data structure and exploit the benefit of Ensemble Learning. The proposed framework is verified through the application of Higher Order Singular Value Decomposition (HOSVD) to the ETH-80 dataset and is shown to outperform the classical Ensemble Learning approach of bootstrap aggregating.
Axelcyrille Ngonga Ngomo - One of the best experts on this subject based on the ideXlab platform.
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Ensemble Learning for named entity recognition
International Semantic Web Conference, 2014Co-Authors: Rene Speck, Axelcyrille Ngonga NgomoAbstract:A considerable portion of the information on the Web is still only available in unstructured form. Implementing the vision of the Semantic Web thus requires transforming this unstructured data into structured data. One key step during this process is the recognition of named entities. Previous works suggest that Ensemble Learning can be used to improve the performance of named entity recognition tools. However, no comparison of the performance of existing supervised machine Learning approaches on this task has been presented so far. We address this research gap by presenting a thorough evaluation of named entity recognition based on Ensemble Learning. To this end, we combine four different state-of-the approaches by using 15 different algorithms for Ensemble Learning and evaluate their performace on five different datasets. Our results suggest that Ensemble Learning can reduce the error rate of state-of-the-art named entity recognition systems by 40%, thereby leading to over 95% f-score in our best run.
Ilia Kisil - One of the best experts on this subject based on the ideXlab platform.
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Tensor Ensemble Learning for Multidimensional Data.
arXiv: Signal Processing, 2018Co-Authors: Ilia Kisil, Ahmad Moniri, Danilo P. MandicAbstract:In big data applications, classical Ensemble Learning is typically infeasible on the raw input data and dimensionality reduction techniques are necessary. To this end, novel framework that generalises classic flat-view Ensemble Learning to multidimensional tensor-valued data is introduced. This is achieved by virtue of tensor decompositions, whereby the proposed method, referred to as tensor Ensemble Learning (TEL), decomposes every input data sample into multiple factors which allows for a flexibility in the choice of multiple Learning algorithms in order to improve test performance. The TEL framework is shown to naturally compress multidimensional data in order to take advantage of the inherent multi-way data structure and exploit the benefit of Ensemble Learning. The proposed framework is verified through the application of Higher Order Singular Value Decomposition (HOSVD) to the ETH-80 dataset and is shown to outperform the classical Ensemble Learning approach of bootstrap aggregating.
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GlobalSIP - TENSOR Ensemble Learning FOR MULTIDIMENSIONAL DATA
2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018Co-Authors: Ilia Kisil, Ahmad Moniri, Danilo P. MandicAbstract:In big data applications, classical Ensemble Learning is typically infeasible on the raw input data and dimensionality reduction techniques are necessary. To this end, novel framework that generalises classic flat-view Ensemble Learning to multidimensional tensor-valued data is introduced. This is achieved by virtue of tensor decompositions, whereby the proposed method, referred to as tensor Ensemble Learning (TEL), decomposes every input data sample into multiple factors which allows for a flexibility in the choice of multiple Learning algorithms in order to improve test performance. The TEL framework is shown to naturally compress multidimensional data in order to take advantage of the inherent multi-way data structure and exploit the benefit of Ensemble Learning. The proposed framework is verified through the application of Higher Order Singular Value Decomposition (HOSVD) to the ETH-80 dataset and is shown to outperform the classical Ensemble Learning approach of bootstrap aggregating.