Book ChapterDOI
New machine learning algorithm: random forest
Yanli Liu,Yourong Wang,Jian Zhang +2 more
- pp 246-252
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TLDR
This Paper gives an introduction of Random Forest, a new Machine Learning Algorithm and a new combination Algorithm that has been wildly used in classification and prediction, and used in regression too.Abstract:
This Paper gives an introduction of Random Forest. Random Forest is a new Machine Learning Algorithm and a new combination Algorithm. Random Forest is a combination of a series of tree structure classifiers. Random Forest has many good characters. Random Forest has been wildly used in classification and prediction, and used in regression too. Compared with the traditional algorithms Random Forest has many good virtues. Therefore the scope of application of Random Forest is very extensive.read more
Citations
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Journal ArticleDOI
Joint Symbol Rate-Modulation Format Identification and OSNR Estimation Using Random Forest Based Ensemble Learning for Intermediate Nodes
TL;DR: In this paper, a joint symbol rate-modulation format identification (SR-MFI) and optical signal-to-noise ratio (OSNR) estimation scheme using the low-bandwidth coherent detecting and random forest (RF)-based ensemble learning is proposed for intermediate nodes in the flexible dense wavelength division multiplexing (F-DWDM) networks.
Proceedings ArticleDOI
Using Combinations of Bio-inspired Feature Selection Algorithms in Software Efforts Estimation: An Empirical Study
Asad Ali,Carmine Gravino +1 more
TL;DR: This paper has investigated the use of various combinations (hybrid) of bio-inspired algorithms with the variety of datasets used and has compared their performance with the state-of-the-art standalone bio- inspired feature selection algorithms.
Journal ArticleDOI
An empirical comparison of validation methods for software prediction models
Asad Ali,Carmine Gravino +1 more
TL;DR: The results reveal that repeated 10‐fold CV with SDEE and optimistic boot with SFP data are the model validation methods that provide a better prediction accuracy in a greater number of experiments than the other model validate methods.
Journal ArticleDOI
Prediction of Charging Demand of Electric City Buses of Helsinki, Finland by Random Forest
Sanchari Deb,Xiaoyu Gao +1 more
TL;DR: In this paper , a random forest-based approach for predicting charging demand was proposed for public e-bus charging demand in the city of Helsinki, Finland, and the proposed method is validated for the prediction.
Journal ArticleDOI
A machine learning approach to evaluate the spatial variability of New York City's 311 street flooding complaints
Candace Agonafir,Tarendra Lakhankar,Reza Khanbilvardi,Nir Y. Krakauer,David B. Radell,Naresh Devineni +5 more
TL;DR: In this article , a Random Forest regression machine learning algorithm is employed to understand how factors affect the spatial variability of street flooding, where the 311 street flooding reports of New York City (NYC) serve as the response, while the explanatory variables include topographic and land feature, physical and population dynamics, locational, infrastructural, and climatic influences.
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
Bagging predictors
TL;DR: Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy.
Proceedings Article
Experiments with a new boosting algorithm
Yoav Freund,Robert E. Schapire +1 more
TL;DR: This paper describes experiments carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems and compared boosting to Breiman's "bagging" method when used to aggregate various classifiers.
Journal ArticleDOI
The random subspace method for constructing decision forests
TL;DR: A method to construct a decision tree based classifier is proposed that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.