Akademik Veri Yönetim Sistemi
Journal of Big Data, cilt.10, sa.38, ss.1-36, 2023 (SCI-Expanded)
The random forest algorithm could be enhanced and produce better results with
a well-designed and organized feature selection phase. The dependency structure
between the variables is considered to be the most important criterion behind selecting
the variables to be used in the algorithm during the feature selection phase. As
the dependency structure is mostly nonlinear, making use of a tool that considers
nonlinearity would be a more beneficial approach. Copula-Based Clustering technique
(CoClust) clusters variables with copulas according to nonlinear dependency. We show
that it is possible to achieve a remarkable improvement in CPU times and accuracy
by adding the CoClust-based feature selection step to the random forest technique.
We work with two different large datasets, namely, the MIMIC-III Sepsis Dataset and
the SMS Spam Collection Dataset. The first dataset is large in terms of rows referring to
individual IDs, while the latter is an example of longer column length data with many
variables to be considered. In the proposed approach, first, random forest is employed
without adding the CoClust step. Then, random forest is repeated in the clusters
obtained with CoClust. The obtained results are compared in terms of CPU time, accuracy
and ROC (receiver operating characteristic) curve. CoClust clustering results are
compared with K-means and hierarchical clustering techniques. The Random Forest,
Gradient Boosting and Logistic Regression results obtained with these clusters and the
success of RF and CoClust working together are examined.