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About 10,000 papers for “ensemble methods”
Zhi-Hua Zhou · 2012
An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field.After presenting background and terminology, the book cover
Cited by 2,279
2014
This chapter illustrates the difference between the performance of a bagging ensemble and a random forest ensemble. AdaBoost is the only ensemble method featured among the “Top 10 algorithms in datamining” by Wu et al. In problems with a large number of features, a natural ensemble-building heuristic is to use different feature subsets to train the ensemble members. Since formally introduced and named random subspace ensemble by Ho, this method has taken the lead in many application domains. The rotation forest ensemble method relies on the instability of the decision tree classifier with respect to rotation of the space. The random linear oracle (RLO) is an ensemble method which combines the classifier fusion and classifier selection approaches. The error correcting output codes (ECOC) ensemble strategy was developed for problems with multiple classes. The idea is to avoid solving the multi-class problem directly and to break it into dichotomies instead.
Cited by 10
2025
Cited by 0Open access
Patrick Juola · 2021
Cited by 0
Karol Przystalski, Maciej Ogorzałek, Jan K. Argasiński et al. · 2025
Cited by 0
Avvisati G · 2000
Cited by 0
Leonardo Vanneschi, Sara Silva · Natural computing series · 2023
Cited by 2
2015
Ensemble methods stem from the observation that multiple models give better performance than a single model if the models are somewhat independent of one another. The key with ensemble methods is to develop an algorithm approach to generate numerous somewhat independent models that will then be combined into an ensemble. This chapter shows the reader how the most popular methods accomplish this. It teaches the mechanics of the most popular ensemble methods, and outlines the basic structure of the algorithms and demonstrates the algorithms in Python code to give a firm understanding of their workings. The chapter primarily uses binary decision trees as base learners. Some widely used upper-level algorithms are known as bagging, gradient boosting, and random forests are coded in this chapter to provide a better understand the options, input variables, nominal starting values, and so on for the Python packages for these algorithms.
Cited by 0
2019
This chapter discusses the mechanics of the most popular ensemble methods. It explains that ensemble methods consist of a hierarchy of two algorithms. Ensemble methods train hundreds or thousands of the low-level algorithms called base learners. The higher-level algorithm controls the training of the base learners in order that their models turn out somewhat independent from one another so that combining them will reduce the variance of the combination. Several upper-level algorithms are widely used. They are bagging, boosting, and random forest. The chapter shows how gradient boosting operates and demonstrates how to control its behavior to get the best performance. The packages available for doing gradient boosting in Python permit you to use random forest base learners with gradient boosting. The chapter explains each of the high-level algorithms and shows a facsimile of the random forest's base learners. It discusses how to measure and regulate overfitting with binary trees.
Cited by 2
Maria Han Veiga, François Gaston Ged · 2024
Cited by 0
J. E. Gubernatis, Naoki Kawashima, Philipp Werner · 2016
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Simon Boutry (10956118), Raphaël Helaers (796995), Tom Lenaerts (21921) et al. · Figshare · 2023
<p>Details of each state-of-the-art method included in our 4 ensemble methods (in green) and the iteration at which the method was added to the ensemble method. In red, the last iteration that failed to meet the criteria of type I error inflated equal to zero (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1011488#pcbi.1011488.s016" target="_blank">S4 Table</a>).</p> <p>(XLSX)</p>
Cited by 0Open access
Albert Bifet, Ricard Gavaldà, Geoffrey Holmes et al. · 2018
Ensemble predictors are combinations of smaller models whose individual predictions are combined in some manner (say, averaging or voting) to form a final prediction. In both batch and streaming scenarios, ensembles tend to improve prediction accuracy over any of their constituents, at the cost of more time and memory resources, and they are often easy to parallelize. But in streaming, ensembles of classifiers have additional advantages over single-classifier methods: they are easy to scale; if mining a distributed stream, they do not require the several streams to be centralized toward a single site; and they can be made to adapt to change by pruning underperforming parts of the ensemble and adding new classifiers. A comprehensive and up-to-date discussion of ensembles for streams is [124].
Cited by 0
Loris Nanni, Sheryl Brahnam, Alessandra Lumini · Wiley Encyclopedia of Electrical and Electronics Engineering · 2015
Multiclassifier systems, the focus of this article, provide scientists and data professionals with powerful techniques for tackling complex datasets. The basic idea behind the multiclassifier approach is to average the decisions or hypotheses of a diverse group of classifiers in order to produce a better decision or hypothesis. As an introduction to our subject, we begin with a detailed examination of the canonic single‐classifier system, as this provides the mathematical foundation needed in our presentation of multiclassifier systems. We then describe some important methods for constructing multiclassifier systems at all the levels mentioned above: the classifier level, the combination level, the data level, and the feature level. We thus end our overview of multiclassifier systems with a section that provides guidance for experimentally constructing general‐purpose (GP) multiclassifier systems.
Cited by 4
Lior Rokach · Medical Entomology and Zoology · 2019
"This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories, trends, challenges, and applications. More than a third of this edition comprised of new materials, highlighting descriptions of the classic methods, and extensions and novel approaches that have recently been introduced. Along with algorithmic descriptions of each method, the settings in which each method is applicable and the consequences and tradeoffs incurred by using the method is succinctly featured. R code for implementation of the algorithm is also emphasized. The unique volume provides researchers, students and practitioners in industry with a comprehensive, concise and convenient resource on ensemble learning methods."--
Cited by 47Open access
Sreedevi Varam · Mendeley Data · 2021
The data repository contains the dataset on high entropy alloys (HEAs) and the code files related to the phase prediction of HEAs using ensemble based machine learning models.
Cited by 0Open access
Lior Rokach · 2006
Cited by 120
Donn Morrison, Ruili Wang, Liyanage C. De Silva · 2007
Cited by 0
Abdulrahman Ghanem (14883426), Ahmed Abdelhay (14883429), Noor Eldeen Salah (14883432) et al. · Figshare · 2023
<div><p>The Global Navigation Satellite System (GNSS) is unreliable in some situations. To mend the poor GNSS signal, an autonomous vehicle can self-localize by matching a ground image against a database of geotagged aerial images. However, this approach has challenges because of the dramatic differences in the viewpoint between aerial and ground views, harsh weather and lighting conditions, and the lack of orientation information in training and deployment environments. In this paper, it is shown that previous models in this area are complementary, not competitive, and that each model solves a different aspect of the problem. There was a need for a holistic approach. An ensemble model is proposed to aggregate the predictions of multiple independently trained state-of-the-art models. Previous state-of-the-art (SOTA) temporal-aware models used heavy-weight network to fuse the temporal information into the query process. The effect of making the query process temporal-aware is explored and exploited by an efficient meta block: naive history. But none of the existing benchmark datasets was suitable for extensive temporal awareness experiments, a new derivative dataset based on the BDD100K dataset is generated. The proposed ensemble model achieves a recall accuracy R@1 (Recall@1: the top most prediction) of 97.74% on the CVUSA dataset and 91.43% on the CVACT dataset (surpassing the current SOTA). The temporal awareness algorithm converges to R@1 of 100% by looking at a few steps back in the trip history.</p></div>
Cited by 0Open access
Matteo Re, Giorgio Valentini · 2001
Cited by 0