.png){kind=icon}
Return
An imprecise deep forest for classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2019.112978.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
An imprecise deep forest classifier, which can be regarded as a modification of the deep forest proposed by Zhou and Feng, is presented in the paper. In the proposed classifier, the precise class probabilities at leaf nodes of decision trees in the deep forest are replaced with interval-valued probabilities produced by Walley's imprecise Dirichlet model. The obtained sets of probabilities are averaged over all trees in a random forest as the imprecise probability masses. In order to use the stacking algorithm in the deep forest, a number of the class probability distributions are generated from sets of the random forest class probabilities such that decision trees of the next level of the deep forest cascade are trained on the basis of the extended training set which is added by new generated class distributions. Numerical examples illustrate how the incorporated imprecision may lead to outperforming results, especially, when the training set is rather small. The proposed classifier is the first modification of the deep forest, which efficiently deals with small datasets and takes into account a lack of sufficient training data. The ideas underlying the classifier may be a basis for development of a set on new machine learning models for small datasets. (C) 2019 Elsevier Ltd. All rights reserved.
Keywords:
Classification
Random forest
Decision tree
Deep forest
Imprecise Dirichlet model
Imprecise probabilities
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
3.0W
Citations:
10.2W
Organization
Cited Papers
ShareSaveReport on the Third Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE3)
OAAI
OAAIShareSave
ShareSaveEnsembles of decision trees based on imprecise probabilities and uncertainty measures.png)
INFORMATION FUSION
INFORMATION FUSIONIF15.5
PREAI
ShareSaveAnalysis and extension of decision trees based on imprecise probabilities: Application on noisy data
PREAI
ShareSaveShareSaveShareSaveShareSave
View more