Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms
Algorithm 2
Prediction process.
(1)
INPUT: query, cl
(2)
//query is the data image to be queried
(3)
//cl represents the number of class for prediction
(4)
MapReduce e data
(5)
Find the feature node to e in the class model and the outputs of a tuple with the class ID (key) and e (value) (MAP)
(6)
The tuple is sent to the correspondent node according to its key (SHUFFLE)
(7)
Features = the standard elements for each image in order to retrieve the classifier model of e. The output will consist of a tuple with e (key) and the classifier model (value) (REDUCE)
(8)
For each tuple in features, the model returns the most-voted class from the classifier model. This value will be the class image predicted for the given image