Evaluation of Semi-supervised Learning for Classification of Protein Crystallization Imagery.
In this paper, we investigate the performance of two wrapper methods for semi-supervised learning algorithms for classification of protein crystallization images with limited labeled images. Firstly, we evaluate the performance of semi-supervised approach using self-training with naïve Bayesian (NB) and sequential minimum optimization (SMO) as the base classifiers. The confidence values returned by these classifiers are used to select high confident predictions to be used for self-training. Secondly, we analyze the performance of Yet Another Two Stage Idea (YATSI) semi-supervised learning using NB, SMO, multilayer perceptron (MLP), J48 and random forest (RF) classifiers. These results are compared with the basic supervised learning using the same training sets. We perform our experiments on a dataset consisting of 2250 protein crystallization images for different proportions of training and test data. Our results indicate that NB and SMO using both self-training and YATSI semi-supervised approaches improve accuracies with respect to supervised learning. On the other hand, MLP, J48 and RF perform better using basic supervised learning. Overall, random forest classifier yields the best accuracy with supervised learning for our dataset.
DataMedia Research Lab, Department of Computer Science, University of Alabama in Huntsville, Huntsville, Alabama 35899, United States.
Madhav
M
Sigdel
DataMedia Research Lab, Department of Computer Science, University of Alabama in Huntsville, Huntsville, Alabama 35899, United States.
İmren
İ
Dinç
DataMedia Research Lab, Department of Computer Science, University of Alabama in Huntsville, Huntsville, Alabama 35899, United States.
Semih
S
Dinç
DataMedia Research Lab, Department of Computer Science, University of Alabama in Huntsville, Huntsville, Alabama 35899, United States.
Madhu S
MS
Sigdel
iXpressGenes, Inc., 601 Genome Way, Huntsville, Alabama 35806, United States.
Marc L
ML
Pusey
DataMedia Research Lab, Department of Computer Science, University of Alabama in Huntsville, Huntsville, Alabama 35899, United States.
Ramazan S
RS
Aygün
YATSI; image classification; protein crystallization; self-training; semi-supervised learning