Evaluation of semi-supervised learning for classification of protein crystallization imagery

Abstract

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.

Predicting Secondary School Students' Performance Utilizing a Semi-supervised Learning Approach

“…More analytically, we evaluated the performances of self-training and Yet Another Two Stage Idea (YATSI) approaches with the most classic classification methods. Self-training and YATSI constitute two of the most efficient and frequently utilized semisupervised algorithms which have been successfully used in a variety of real-world applications (Catal & Diri, 2009;Driessens, Reutemann, Pfahringer, & Leschi, 2006;Levatic, Dzeroski, Supek, & Smuc, 2013;Roli & Marcialis 2006;Rosenberg, Hebert, & Schneiderman, 2005;Sigdel et al, 2014) providing some promising classification results. Our preliminary numerical experiments illustrate that the classification accuracy can be significantly improved, utilizing a few labeled and many unlabeled data for developing reliable prediction models.…”

An Ensemble SSL Algorithm for Efficient Chest X-Ray Image Classification

“…In the literature, a variety of self-labeled methods has been proposed each with a different philosophy and methodology on exploiting the information hidden in the unlabeled data. In this work, we focus our attention on self-training, co-training and tri-training, which constitute the most useful and commonly-used self-labeled methods [12,16,20,21]. Notice that the crucial difference between them is the mechanism used to label unlabeled data.…”

“…It has been established as a very popular algorithm due to its simplicity, and it is often found to be more accurate than other semi-supervised algorithms [16,20,23]. In the self-training framework, an arbitrary classifier is initially trained with a small amount of labeled data, which comprise its training set, aiming to classify unlabeled points.…”

An Auto-Adjustable Semi-Supervised Self-Training Algorithm

“…Hence, these methods have the advantage of reducing the effort of supervision to a minimum, while still preserving competitive recognition performance. Nowadays, these algorithms have great interest both in theory and in practice and have become a topic of significant research as an alternative to traditional methods of machine learning, since they require less human effort and frequently present higher accuracy [4][5][6][7][8][9][10]. The main issue of semi-supervised learning is how to efficiently exploit the hidden information in the unlabeled data.…”

“…Self-training constitutes perhaps the most popular and frequently used SSL algorithm due to its simplicity and classification accuracy [4,5,9]. This algorithm wraps around a base learner and uses its own predictions to assign labels to unlabeled data.…”