Journal ArticleDOI
Ensemble learning: A survey
Omer Sagi,Lior Rokach +1 more
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TLDR
The concept of ensemble learning is introduced, traditional, novel and state‐of‐the‐art ensemble methods are reviewed and current challenges and trends in the field are discussed.Abstract:
Ensemble methods are considered the state‐of‐the art solution for many machine learning challenges. Such methods improve the predictive performance of a single model by training multiple models and combining their predictions. This paper introduce the concept of ensemble learning, reviews traditional, novel and state‐of‐the‐art ensemble methods and discusses current challenges and trends in the field.read more
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Journal ArticleDOI
A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources
TL;DR: This work popularizes RF and their variants for the practicing water scientist, and discusses related concepts and techniques, which have received less attention from the water science and hydrologic communities.
Journal ArticleDOI
CatBoost for big data: an interdisciplinary review
TL;DR: This survey takes an interdisciplinary approach to cover studies related to CatBoost in a single work, and provides researchers an in-depth understanding to help clarify proper application of Cat boost in solving problems.
Journal ArticleDOI
A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities
TL;DR: The performance of 14 different bagging and boosting based ensembles, including XGBoost, LightGBM and Random Forest, is empirically analyzed in terms of predictive capability and efficiency.
Journal ArticleDOI
Machine Learning Based Intrusion Detection Systems for IoT Applications
Abhishek Verma,Virender Ranga +1 more
TL;DR: The main goals of this study are to motivate IoT security researchers for developing IDSs using ensemble learning, and suggesting appropriate methods for statistical assessment of classifier’s performance.
Posted Content
Ensemble deep learning: A review.
TL;DR: Deep Ensemble Learning (DEL) as mentioned in this paper combines several individual models to obtain better generalization performance by combining the advantages of both the deep learning models as well as the ensemble learning.
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Journal Article
Dropout: a simple way to prevent neural networks from overfitting
TL;DR: It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.
Journal ArticleDOI
Greedy function approximation: A gradient boosting machine.
TL;DR: A general gradient descent boosting paradigm is developed for additive expansions based on any fitting criterion, and specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification.
Proceedings ArticleDOI
XGBoost: A Scalable Tree Boosting System
Tianqi Chen,Carlos Guestrin +1 more
TL;DR: XGBoost as discussed by the authors proposes a sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning to achieve state-of-the-art results on many machine learning challenges.