Results 11 to 20 of about 1,038,727 (324)
Ecological IndicatorsA simpler, unbiased, and comprehensive random forest (RF) model is needed to improve the accuracy of aboveground biomass (AGB) estimation. In this study, data were obtained from 128 sample plots of Pinus yunnanensis forest located in Chuxiong prefecture,
Xiaoli Zhang +11 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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Double Cost Sensitive Random Forest Algorithm
Journal of Harbin University of Science and Technology, 2021 A Double Cost Sensitive Random Forest (DCS-RF) algorithm is proposed to solve the problem that the accuracy of a few classes is not ideal when the classifier identifies unbalanced data.
ZHOU Yan-long, SUN Guang-lu
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Corporate Distress Prediction Using Random Forest and Tree Net for India [PDF]
, 2020 The paper deals with the Random Forest, a popular classification machine learning algorithm to predict bankruptcy (distress) for Indian firms. Random Forest orders firms according to their propensity to default or their likelihood to become distressed ...
Gupta, Sanjeev +3 more
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Random Forest Spatial Interpolation
Remote Sensing, 2020 For many decades, kriging and deterministic interpolation techniques, such as inverse distance weighting and nearest neighbour interpolation, have been the most popular spatial interpolation techniques.
Aleksandar Sekulić +4 more
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A Novel Consistent Random Forest Framework: Bernoulli Random Forests [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2018 Random forests (RFs) are recognized as one type of ensemble learning method and are effective for the most classification and regression tasks. Despite their impressive empirical performance, the theory of RFs has yet been fully proved. Several theoretically guaranteed RF variants have been presented, but their poor practical performance has been ...
Yisen Wang +4 more
openaire +2 more sources
Feature-Weighting and Clustering Random Forest
International Journal of Computational Intelligence Systems, 2020 Classical random forest (RF) is suitable for the classification and regression tasks of high-dimensional data. However, the performance of RF may be not satisfied in case of few features, because univariate split method cannot bring more diverse ...
Zhenyu Liu +3 more
doaj +1 more source
Portfolio Selection Using Random Forest Algorithm
International Journal of Computer Engineering and Data Science, 2022 Portfolio selection has long been a main topic in finance. What stocks should one invest in? How much should one allocate to each stock to maximize gain and minimize risk?
Daname KOLANI
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RFDCR:Automated brain lesion segmentation using cascaded random forests with dense conditional random fields [PDF]
, 2020 Segmentation of brain lesions from magnetic resonance images (MRI) is an important step for disease diagnosis, surgical planning, radiotherapy and chemotherapy.
Chen, Gaoxiang +5 more
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Random forest for gene selection and microarray data classification [PDF]
, 2011 A random forest method has been selected to perform both gene selection and classification of the microarray data. The goal of this research is to develop and improve the random forest gene selection method.
Moorthy, Kohbalan +1 more
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