A comparison of random forest based algorithms: random credal random forest versus oblique random forest

Mantas, Carlos Javier; Castellano, Javier G.; Moral-García, Serafín; Abellán, Joaquín

doi:10.1007/s00500-018-3628-5

Cited by 78 publications

(38 citation statements)

References 36 publications

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“…The accuracy of the prediction of critical lands indicates that the RF algorithm has the highest accuracy, while the lowest is the NB algorithm. It confirms [24], [25] that RF is an ensemble learning method and considered a reference due to its excellent performance.…”

Section: Resultssupporting

confidence: 72%

Comparative analysis of classification algorithms for critical land prediction in agricultural cultivation areas

Istiawan

2020

Jurnal Teknologi dan Sistem Komputer

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Currently, the identification of critical land, that has been physically, chemically, and biologically damaged, uses a geographic information system. However, it requires a high cost to get the high resolution of satellite images. In this study, a comparison framework is proposed to determine the performance of the classification algorithms, namely C.45, ID3, Random Forest, k-Nearest Neighbor, and Naïve Bayes. This research aims to find out the best algorithm for the classification of critical land in agricultural cultivation areas. The results show that the highest accuracy Random Forest algorithm was 93.10 % in predicting critical land, and the naïve Bayes has the lowest performance, with 89.32 % of accuracy in predicting critical land.

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Section: Resultssupporting

confidence: 72%

Comparative analysis of classification algorithms for critical land prediction in agricultural cultivation areas

Istiawan

2020

Jurnal Teknologi dan Sistem Komputer

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“…A Random Forest consists of randomly generated decision trees which are independent of each other [36]. We need to adjust two parameters for the trees: -1) the tree count and 2) count of features to be used for generating the trees.…”

Section: Model Selectionmentioning

confidence: 99%

Predicting Success of Campaigns on Membership based Patreon Crowdfunding Platform

Mukherjee¹,

Karvekar²,

Badr³

2022

IJCDS

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Crowdfunding platforms, such as the Patreon platform, are a means of regular financial support to entrepreneurs and artists who create independent content in the form of images, videos, podcasts, comics, games, or any media that supporters enjoy. Entrepreneurs leverage their potential base of patrons by using various social media platforms. Even though this collaboration has proved to be a practical approach to raising funds, it is difficult to predict the success rates of new projects. In this paper, we consider Patreon as the membership-based platform and our empirical analysis shows that half of proposed projects turn out to be successful. In this research, we build a data analytics approach to predict the rate of success of Patreon projects based on a dataset containing details of various features and historical information about previous projects. We employed a family of supervised classifiers that includes Naïve Bayes, Logistic Regression, Random Forest, and Boosting algorithms to predict the success of a given project. Currently, the Gradient Boosting classifier has achieved an average accuracy of more than 74%. Such results could help creators to define a path to better promote their content and improve monthly pledges.

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“…The basic unit of the RAF model is the decision tree. Its essence is a part of machine ensemble learning, and it is an algorithm that integrates holistic learning with multiple trees (Bernard et al, 2008;Mantas et al, 2018;Vens, 2013). Because the model has the advantages of generating high accuracy classifiers, not easy to over fitting, excellent anti-noise ability, and fast training speed, the RaF algorithm is widely utilized in difficult prediction work, especially in nonlinear high-dimensional landslide spatial evaluation (Dang et al, 2018).…”

Section: Random Forest (Raf)mentioning

confidence: 99%

Comparison of rotation forest based intelligence models for landslide susceptibility modeling

Zhao¹,

Chen²,

Li³

et al. 2021

Preprint

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The precision of landslide susceptibility assessment has always been the focus of landslide spatial prediction research. It can be considered as the possibility of landslide disaster under the action of human activities or natural factors, or both of them. For the further exploration of the mechanism of this process, Muchuan County was proposed as the study area, and four well-known machine learning models, namely rotation forest (RF), J48 decision tree (J48), alternating decision tree (ADTree) and random forest (RaF), and their ensembles (RF-J48, RF-ADTree and RF-RaF) were introduced to explore the mechanism. These models are established by twelve landslide conditioning factors, which are selected based to the local special geological environment conditions and previous related researches, including plan curvature, profile curvature, slope angle, slope aspect, elevation, topographic wetness index (TWI), land use, normalized difference vegetation index (NDVI), soil, lithology, distance to roads, and distance to rivers, as well as training (195 landslides) and validation (84 landslides) datasets were developed. The landslide prediction performance of the above conditioning factors was analyzed through the correlation attribute evaluation (CAE) model. Then, through the landslide susceptibility maps made by the above six different models, the Jenks natural breaks method is used to divide the landslide susceptibility into five grades, which are very low, low, moderate, high, and very high. In addition, the accuracy of the above six landslide susceptibility maps was verified by implementing the relative operating characteristic curve (ROC) and the area under the ROC (AUC). That is, the capabilities of the above six models are compared and verified in the landslide spatial prediction. Finally, the obtained results show that elevation, lithology and TWI are the three most principal landslide conditioning factors in this research. The RF-RaF and RaF models in the training dataset performed best, with the AUC value of 0.75, while the RF-ADTree model (0.74), RF-J48 model (0.74), ADTree model (0.71) and J48 model (0.70) performed poorly. Meanwhile, similar results also emerge from the validation dataset, in which the RF-RaF model acquired the best performance (0.82) and the rest are the RF-ADTree model (0.80), RaF model (0.79), RF-J48 model (0.77), ADTree model (0.76) and J48 model (0.71). Last but by no means the least, the results can provide scientific references for local natural resources departments.

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A comparison of random forest based algorithms: random credal random forest versus oblique random forest

Cited by 78 publications

References 36 publications

Comparative analysis of classification algorithms for critical land prediction in agricultural cultivation areas

Comparative analysis of classification algorithms for critical land prediction in agricultural cultivation areas

Predicting Success of Campaigns on Membership based Patreon Crowdfunding Platform

Comparison of rotation forest based intelligence models for landslide susceptibility modeling

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