Two-stage Hybrid Classifier Ensembles for Subcellular Phenotype Images Classification

An automatic, reliable and efficient prediction system for protein subcellular localization can be used for establishing knowledge of the spatial distribution of proteins within living cells and permits to screen systems for drug discovery or for early diagnosis of a disease. In this paper, we propose a two-stage multiple classifier system to improve classification reliability by introducing rejection option. The system is built as a cascade of two classifier ensembles. The first ensemble consists of set of binary SVMs which generalizes to learn a general classification rule and the second ensemble focus on the exceptions rejected by the rule. To enhance diversity for the classifier ensembles, multiple features are introduced, including the local binary patterns (LBP), Gabor filtering and Gray Level Coocurrence Matrix (GLCM). Using the public benchmark 2D HeLa cell images, a high classification accuracy 96% is obtained with rejection rate 21%.