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Number of classifiers in ensembles before and after evolution.

NIAID Data Ecosystem2026-03-07 收录

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分类器优化

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https://figshare.com/articles/dataset/_Number_of_classifiers_in_ensembles_before_and_after_evolution_/736416 数据链接链接失效反馈

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G means it is a global classifier and L indicates a local classifier.

应用场景：

创建时间：

2013-07-01

多标签分类

集成学习

In this work, we develop a new ensemble learning framework, multi-label Random Subspace Ensemble (mRaSE), for multi-label classification. Given a base classifier (e.g., multinomial logistic reg

DataCite Commons2024-10-28 更新200

Bayesian Ensemble Trees (BET) for Clustering and Prediction in Heterogeneous Data

集成学习

医疗数据分析

We propose a novel “tree-averaging” model that uses the ensemble of classification and regression trees (CART). Each constituent tree is estimated with a subset of similar data. We treat this grouping

Mendeley Data2024-06-25 更新60

Performance comparison between individual models and the ensemble approach in the local and external validation scenarios.

模型验证

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Performance comparison between individual models and the ensemble approach in the local and external validation scenarios.

NIAID Data Ecosystem50

Data S5. Ensemble results figures for each clade

生态建模

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Ensemble results for single clade Habitat Suitability Modelling for each scenario.

DataCite Commons2020-08-25 更新80

Code for Cheplygina et al - Dissimilarity-based ensembles for multiple instance learning, TNNLS 2016

多重实例学习

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This code is used in the papers: Cheplygina, V., Tax, D. M., & Loog, M. (2016). Dissimilarity-based ensembles for multiple instance learning. IEEE transactions on neural networks and learning s

DataCite Commons2020-08-28 更新50

Number of classifiers in ensembles before and after evolution.

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