  One of the most important problems in the field of pattern recognition is
data classification. Due to the increasing development of technologies
introduced in the field of data classification, some of the solutions are still
open and need more research. One of the challenging problems in this area is
the curse of dimensionality of the feature set of the data classification
problem. In solving the data classification problems, when the feature set is
too large, typical approaches will not be able to solve the problem. In this
case, an approach can be used to partition the feature set into multiple
feature sub-sets so that the data classification problem is solved for each of
the feature subsets and finally using the ensemble classification, the
classification is applied to the entire feature set. In the above-mentioned
approach, the partitioning of feature set into feature sub-sets is still an
interesting area in the literature of this field. In this research, an
innovative framework for multi-view ensemble classification, inspired by the
problem of object recognition in the multiple views theory of humans, is
proposed. In this method, at first, the collaboration values between the
features is calculated using a criterion called the features collaboration
criterion. Then, the collaboration graph is formed based on the calculated
collaboration values. In the next step, using the community detection method,
graph communities are found. The communities are considered as the problem
views and the different base classifiers are trained for different views using
the views corresponding training data. The multi-view ensemble classifier is
then formed by a combination of base classifiers based on the AdaBoost
algorithm. The simulation results of the proposed method on the real and
synthetic datasets show that the proposed method increases the classification
accuracy.
