  The decision tree ensembles use a single data feature at each node for
splitting the data. However, splitting in this manner may fail to capture the
geometric properties of the data. Thus, oblique decision trees generate the
oblique hyperplane for splitting the data at each non-leaf node. Oblique
decision trees capture the geometric properties of the data and hence, show
better generalization. The performance of the oblique decision trees depends on
the way oblique hyperplanes are generate and the data used for the generation
of those hyperplanes. Recently, multiple classifiers have been used in a
heterogeneous random forest (RaF) classifier, however, it fails to generate the
trees of proper depth. Moreover, double RaF studies highlighted that larger
trees can be generated via bootstrapping the data at each non-leaf node and
splitting the original data instead of the bootstrapped data recently. The
study of heterogeneous RaF lacks the generation of larger trees while as the
double RaF based model fails to take over the geometric characteristics of the
data. To address these shortcomings, we propose heterogeneous oblique double
RaF. The proposed model employs several linear classifiers at each non-leaf
node on the bootstrapped data and splits the original data based on the optimal
linear classifier. The optimal hyperplane corresponds to the models based on
the optimized impurity criterion. The experimental analysis indicates that the
performance of the introduced heterogeneous double random forest is
comparatively better than the baseline models. To demonstrate the effectiveness
of the proposed heterogeneous double random forest, we used it for the
diagnosis of Schizophrenia disease. The proposed model predicted the disease
more accurately compared to the baseline models.
