  The constant growth in the present day real-world databases pose
computational challenges for a single computer. Cloud-based platforms, on the
other hand, are capable of handling large volumes of information manipulation
tasks, thereby necessitating their use for large real-world data set
computations. This work focuses on creating a novel Generalized Flow within the
cloud-based computing platform: Microsoft Azure Machine Learning Studio (MAMLS)
that accepts multi-class and binary classification data sets alike and
processes them to maximize the overall classification accuracy. First, each
data set is split into training and testing data sets, respectively. Then,
linear and nonlinear classification model parameters are estimated using the
training data set. Data dimensionality reduction is then performed to maximize
classification accuracy. For multi-class data sets, data centric information is
used to further improve overall classification accuracy by reducing the
multi-class classification to a series of hierarchical binary classification
tasks. Finally, the performance of optimized classification model thus achieved
is evaluated and scored on the testing data set. The classification
characteristics of the proposed flow are comparatively evaluated on 3 public
data sets and a local data set with respect to existing state-of-the-art
methods. On the 3 public data sets, the proposed flow achieves 78-97.5%
classification accuracy. Also, the local data set, created using the
information regarding presence of Diabetic Retinopathy lesions in fundus
images, results in 85.3-95.7% average classification accuracy, which is higher
than the existing methods. Thus, the proposed generalized flow can be useful
for a wide range of application-oriented "big data sets".
