The Effects of Targeting Predictors in a Random Forest Regression Model

Borup, Daniel; Christensen, Bent Jesper; Søndergaard, Mühlbach, Nicolaj; Nielsen, Mikkel Slot

doi:10.2139/ssrn.3551557

Cited by 7 publications

(4 citation statements)

References 49 publications

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“…Modeling consumer credit risk by RF is the main goal of [ 13 ]. 14 increase tree correlation by controlling the probability of placing splits along with strong predictors to deal with high-dimensional settings. Sikdar et al [ 15 ] proposed a variable selection method based on RF to identify the key predictors of price change in amazon.…”

Section: Related Workmentioning

confidence: 99%

“…Computational Intelligence and Neuroscience 9) Algorithm (10) For r � 1 to R do (11) Create a dataset D t , by sampling (N/R) items, randomly with replacement from D (12) Train DT r using D t , and add to the pool (13) end//for (14) Select one of the actions at random and execute it, by the LA, Let it be α i (20) If (α i predicts the new test sample correctly) then//Update the probability of selection vector (21) p…”

Section: R I�1mentioning

confidence: 99%

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A New Random Forest Algorithm Based on Learning Automata

Savargiv

Masoumi

Keyvanpour

2021

Computational Intelligence and Neuroscience

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The goal of aggregating the base classifiers is to achieve an aggregated classifier that has a higher resolution than individual classifiers. Random forest is one of the types of ensemble learning methods that have been considered more than other ensemble learning methods due to its simple structure, ease of understanding, as well as higher efficiency than similar methods. The ability and efficiency of classical methods are always influenced by the data. The capabilities of independence from the data domain, and the ability to adapt to problem space conditions, are the most challenging issues about the different types of classifiers. In this paper, a method based on learning automata is presented, through which the adaptive capabilities of the problem space, as well as the independence of the data domain, are added to the random forest to increase its efficiency. Using the idea of reinforcement learning in the random forest has made it possible to address issues with data that have a dynamic behaviour. Dynamic behaviour refers to the variability in the behaviour of a data sample in different domains. Therefore, to evaluate the proposed method, and to create an environment with dynamic behaviour, different domains of data have been considered. In the proposed method, the idea is added to the random forest using learning automata. The reason for this choice is the simple structure of the learning automata and the compatibility of the learning automata with the problem space. The evaluation results confirm the improvement of random forest efficiency.

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Section: Related Workmentioning

confidence: 99%

Section: R I�1mentioning

confidence: 99%

A New Random Forest Algorithm Based on Learning Automata

Savargiv

Masoumi

Keyvanpour

2021

Computational Intelligence and Neuroscience

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“…Notwithstanding, some authors have noted that a trade-off emerges between how focused a RF is and its robustness via diversification Borup et al (2020). sometimes get improvements over plain RF by adding a Lasso preprocessing step to trim X.…”

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confidence: 99%

Macroeconomic data transformations matter

Coulombe

Leroux

Stevanović

et al. 2021

International Journal of Forecasting

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The purpose of the Working Papers is to disseminate the results of research conducted by CIRANO research members in order to solicit exchanges and comments. These reports are written in the style of scientific publications. The ideas and opinions expressed in these documents are solely those of the authors.Les cahiers de la série scientifique visent à rendre accessibles les résultats des recherches effectuées par des chercheurs membres du CIRANO afin de susciter échanges et commentaires. Ces cahiers sont rédigés dans le style des publications scientifiques et n'engagent que leurs auteurs.CIRANO is a private non-profit organization incorporated under the Quebec Companies Act. Its infrastructure and research activities are funded through fees paid by member organizations, an infrastructure grant from the government of Quebec, and grants and research mandates obtained by its research teams. Le CIRANO est un organisme sans but lucratif constitué en vertu de la Loi des compagnies duQuébec. Le financement de son infrastructure et de ses activités de recherche provient des cotisations de ses organisations-membres, d'une subvention d'infrastructure du gouvernement du Québec, de même que des subventions et mandats obtenus par ses équipes de recherche. CIRANO Partners -Les partenaires du CIRANO Corporate Partners -Partenaires corporatifs Autorité des marchés financiers Bank of Canada Bell Canada BMO Financial Group Business Development Bank of Canada Caisse de dépôt et placement du Québec

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“…In recent macro forecasting work using RF, Goulet Coulombe et al (2019) follow a dense approach by only including factors in the regression whileBorup et al (2020) put their money on sparsity by proposing a Lasso pre-processing step using the raw data.16 That problem has been documented inBai and Ng (2008) and others.…”

mentioning

confidence: 99%

The Macroeconomy as a Random Forest

Coulombe

2020

SSRN Journal

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The Effects of Targeting Predictors in a Random Forest Regression Model

Cited by 7 publications

References 49 publications

A New Random Forest Algorithm Based on Learning Automata

A New Random Forest Algorithm Based on Learning Automata

Macroeconomic data transformations matter

The Macroeconomy as a Random Forest

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