ArticleComputational intelligence and neuroscience2015
Toward a General-Purpose Heterogeneous Ensemble for Pattern Classification.
Article in Computational intelligence and neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Artificial intelligence strengthens cervical cancer screening - present and future.Cancer biology & medicine · 2024Review
- Forecasting the seasonal dynamics of Trichoplusia ni (Lep.: Noctuidae) on three Brassica crops through neural networks.International journal of biometeorology · 2022Article
- Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines.Computational intelligence and neuroscience · 2017Article
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Authors and funding
4 authors.
Funding
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Abstract
We perform an extensive study of the performance of different classification approaches on twenty-five datasets (fourteen image datasets and eleven UCI data mining datasets). The aim is to find General-Purpose (GP) heterogeneous ensembles (requiring little to no parameter tuning) that perform competitively across multiple datasets. The state-of-the-art classifiers examined in this study include the support vector machine, Gaussian process classifiers, random subspace of adaboost, random subspace of rotation boosting, and deep learning classifiers. We demonstrate that a heterogeneous ensemble based on the simple fusion by sum rule of different classifiers performs consistently well across all twenty-five datasets. The most important result of our investigation is demonstrating that some very recent approaches, including the heterogeneous ensemble we propose in this paper, are capable of outperforming an SVM classifier (implemented with LibSVM), even when both kernel selection and SVM parameters are carefully tuned for each dataset.
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