Framework for a Stimulated Predictive Distributed Learning Method

Authors

Fisher’s discriminant ratio, Stability index, Ensemble Learning Methods, Supervised algorithm, Distributed Feature Selection method

Due to the intrinsic properties of high-dimensional microarray datasets, most feature selection approaches do not

scale well, which makes these models inapplicable and impairs the performance of most classifiers. This study

used data complexity and stability measures to maintain class distribution and reduce features variability while

proposing a novel predictive distributed FS model through horizontal partitioning. Brain tumour microarray

benchmark was employed for implementation. Six classifiers as well as feature selection methods were

employed along with their ensemble learning techniques. The study observed the proposed distributed model

with an average accuracy of 98.54% and 99.67% obtained from both the single and ensemble

models respectively.