Outlier Prediction Using Random Forest Classifier

Random forest is an ensemble learning method for classification, regression and other tasks that operate by constructing a multiple decision trees using training data and majority of the class will be consider as output. Out-of-Bag (OOB) takes the samples from the training set with replacement. In r...

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Bibliographic Details
Published in2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC) pp. 0027 - 0033
Main Authors Mohandoss, Divya Pramasani, Shi, Yong, Suo, Kun
Format Conference Proceeding
LanguageEnglish
Published IEEE 27.01.2021
Subjects
Data models
LSTM
OOB
Random forests
Testing
Training
Training data
Vegetation
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DOI10.1109/CCWC51732.2021.9376077

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Summary:Random forest is an ensemble learning method for classification, regression and other tasks that operate by constructing a multiple decision trees using training data and majority of the class will be consider as output. Out-of-Bag (OOB) takes the samples from the training set with replacement. In random forests, if you choose oob to true then there is no need for a separate test set to validate the model. It is estimated internally when the forest is built on training data, and each tree is tested on one-third of the samples not used in building that tree. Out of bag estimate an internal estimate of a random forest as it is being constructed. In this paper we propose two approach to implement outlier prediction by applying random forest classifier and LSTM model with experiments.
DOI:10.1109/CCWC51732.2021.9376077