Results 31 to 40 of about 3,227,878 (310)
Sankhya A, 2018 Given an ensemble of randomized regression trees, it is possible to restructure them as a collection of multilayered neural networks with particular connection weights. Following this principle, we reformulate the random forest method of Breiman (2001) into a neural network setting, and in turn propose two new hybrid procedures that we call neural ...
Biau, Gérard +2 more
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Random Forest variable importance with missing data [PDF]
, 2012 Random Forests are commonly applied for data prediction and interpretation. The latter purpose is supported by variable importance measures that rate the relevance of predictors. Yet existing measures can not be computed when data contains missing values.
Hapfelmeier, Alexander +2 more
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Sentiment Analysis With Sarcasm Detection On Politician’s Instagram
IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2021 Sarcasm is one of the problem that affect the result of sentiment analysis. According to Maynard and Greenwood (2014), performance of sentiment analysis can be improved when sarcasm also identified. Some research used Naïve Bayes and Random Forest method
Aisyah Muhaddisi +2 more
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Machine Learning, 2020 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sunwoo Han, Hyunjoong Kim, Yung-Seop Lee
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An AUC-based Permutation Variable Importance Measure for Random Forests [PDF]
, 2012 The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs).
Silke Janitza +5 more
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AUC-RF: A New Strategy for Genomic Profiling with Random Forest [PDF]
, 2011 Objective: Genomic profiling, the use of genetic variants at multiple loci simultaneously for the prediction of disease risk, requires the selection of a set of genetic variants that best predicts disease status.
Luz Calle, M. +10 more
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Archives of Medical Science, 2021 Introduction It is essential to predict the survival status of patients based on their prognosis. This can assist physicians in evaluating treatment decisions. Random forest is an excellent machine learning algorithm even without any modification.
Cheng Xu +4 more
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Pattern Recognition Letters, 2012 In this paper, we introduce a new Random Forest (RF) induction algorithm called Dynamic Random Forest (DRF) which is based on an adaptative tree induction procedure. The main idea is to guide the tree induction so that each tree will complement as much as possible the existing trees in the ensemble.
Simon Bernard 0001 +2 more
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BarekengRandom forest algorithm allows for building better CART models. However, the disadvantage of this method is often underfitting, especially for small node sizes. Therefore, the double random forest method was developed to overcome this problem.
Arie Purwanto +2 more
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Random Forest Algorithm Based on Data Integration [PDF]
Jisuanji gongcheng, 2020 The historical data used for sales forecasting has the characteristics of sparseness and volatility,the traditional statistical or machine learning prediction algorithms for prediction perform poorly when the prediction cycle is long.Therefore,based on ...
XIE Kun, RONG Yutian, HU Fengping, CHEN Huan, YAO Xiaolong
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