  Chimera state refers to coexistence of coherent and non-coherent phases in
identically coupled dynamical units found in various complex dynamical systems.
Identification of Chimera, on one hand is essential due to its applicability in
various areas including neuroscience, and on other hand is challenging due to
its widely varied appearance in different systems and the peculiar nature of
its profile. Therefore, a simple yet universal method for its identification
remains an open problem. Here, we present a very distinctive approach using
machine learning techniques to characterize different dynamical phases and
identify the chimera state from given spatial profiles generated using various
different models. The experimental results show that the performance of the
classification algorithms varies for different dynamical models. The machine
learning algorithms, namely random forest, oblique random forest based on
tikhonov, parallel-axis split and null space regularization achieved more than
$96\% $ accuracy for the Kuramoto model. For the logistic-maps, random forest
and tikhonov regularization based oblique random forest showed more than $90\%$
accuracy, and for the H\'enon-Map model, random forest, null-space and
axis-parallel split regularization based oblique random forest achieved more
than $80\%$ accuracy. The oblique random forest with null space regularization
achieved consistent performance (more than $83\%$ accuracy) across different
dynamical models while the auto-encoder based random vector functional link
neural network showed relatively lower performance. This work provides a
direction for employing machine learning techniques to identify dynamical
patterns arising in coupled non-linear units on large-scale, and for
characterizing complex spatio-temporal patterns in real-world systems for
various applications.
