An optimization-based decision tree approach for predicting slip-trip-fall accidents at work
Abstract Slip-trip-fall (STF) accident is one of the leading causes of injuries. Therefore,
prediction of STF is necessary prior to its occurrence at workplaces. Although there exist a …
prediction of STF is necessary prior to its occurrence at workplaces. Although there exist a …
Rule extraction from random forest: the RF+ HC methods
M Mashayekhi, R Gras - Advances in Artificial Intelligence: 28th Canadian …, 2015 - Springer
Random forest (RF) is a tree-based learning method, which exhibits a high ability to
generalize on real data sets. Nevertheless, a possible limitation of RF is that it generates a …
generalize on real data sets. Nevertheless, a possible limitation of RF is that it generates a …
Hybrid feature selection using correlation coefficient and particle swarm optimization on microarray gene expression data
A Chinnaswamy, R Srinivasan - … and Applications: Proceedings of the 6th …, 2016 - Springer
Diagnosis of cancer is one of the most emerging clinical applications in microarray gene
expression data. However, cancer classification on microarray gene expression data still …
expression data. However, cancer classification on microarray gene expression data still …
Rule extraction from decision trees ensembles: new algorithms based on heuristic search and sparse group lasso methods
M Mashayekhi, R Gras - International Journal of Information …, 2017 - World Scientific
Decision trees are examples of easily interpretable models whose predictive accuracy is
normally low. In comparison, decision tree ensembles (DTEs) such as random forest (RF) …
normally low. In comparison, decision tree ensembles (DTEs) such as random forest (RF) …
Two-stage rule extraction method based on tree ensemble model for interpretable loan evaluation
L Dong, X Ye, G Yang - Information Sciences, 2021 - Elsevier
The tree ensemble model has been widely employed as a loan evaluation method in credit
risk assessment due to its high accuracy and robustness. However, the tree ensemble …
risk assessment due to its high accuracy and robustness. However, the tree ensemble …
Forex++: A new framework for knowledge discovery from decision forests
Decision trees are popularly used in a wide range of real world problems for both prediction
and classification (logic) rules discovery. A decision forest is an ensemble of decision trees …
and classification (logic) rules discovery. A decision forest is an ensemble of decision trees …
Neural network rule extraction by a new ensemble concept and its theoretical and historical background: A review
Y Hayashi - International Journal of Computational Intelligence and …, 2013 - World Scientific
This paper presents theoretical and historical backgrounds related to neural network rule
extraction. It also investigates approaches for neural network rule extraction by ensemble …
extraction. It also investigates approaches for neural network rule extraction by ensemble …
Learning accurate and interpretable models based on regularized random forests regression
Background Many biology related research works combine data from multiple sources in an
effort to understand the underlying problems. It is important to find and interpret the most …
effort to understand the underlying problems. It is important to find and interpret the most …
Interpretability via random forests
Although there is no consensus on a precise definition of interpretability, it is possible to
identify several requirements:“simplicity, stability, and accuracy”, rarely all satisfied by …
identify several requirements:“simplicity, stability, and accuracy”, rarely all satisfied by …
Pattern‐oriented analysis of system dynamics models via random forests
M Edali - System Dynamics Review, 2022 - Wiley Online Library
Abstract System dynamics (SD) modeling studies aim to reveal the causes of problematic
dynamic behaviors and eliminate them through policy design and analysis. The analyst …
dynamic behaviors and eliminate them through policy design and analysis. The analyst …