Parallel classifiers ensemble with hierarchical machine learning for imbalanced classes

Imbalanced distributions and mis-classified costs of two classes made conventional classification methods suffered. This paper proposed a new fast parallel classification method for imbalanced classes. Considering imbalanced distributions, the approach adopted a fast simple classifier with less features input working parallel with a complicated one. Most samples would be correctly recognized by the first classifier, and the second relatively slower classifier could be ended. The second one was only trained and worked for less difficult samples. Experimental results in machine vision quality inspection showed that the approach could effectively improve classification speed and decrease total risk for imbalanced classespsila classification.