Subcellular Phenotype Images Classification by MLP Ensembles with Random Linear Oracle

Subcellular localization is a key functional characteristic of proteins. 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 investigate an approach based on augmented image features by incorporating curvelet transform and neural network (MLP) ensemble for classification. A simple Random Subspace (RS) ensemble offers satisfactory performance, which contains a set of base MLP classifiers trained with subsets of attributes randomly drawn from the combined features of curvelet coefficients and original Subcellular Location Features (SLF). An MLP ensemble with Random Linear Oracle (RLO) can further improve the performance by replacing a base classifier with a "miniensemble", which consists of a pair of base classifiers and a fixed, randomly created oracle that selects between them. With the benchmarking 2D HeLa images, our experiments show the effectiveness of the proposed approach. The RS-MLP ensemble offers the classification rate 95% while the RS-RLO ensemble gives 95.7% accuracy, which compares sharply with the previously published benchmarking result 84%.