Abstract: Learning using labeled and unlabelled data has
received considerable amount of attention in the machine learning
community due its potential in reducing the need for expensive
labeled data. In this work we present a new method for combining
labeled and unlabeled data based on classifier ensembles. The model
we propose assumes each classifier in the ensemble observes the
input using different set of features. Classifiers are initially trained
using some labeled samples. The trained classifiers learn further
through labeling the unknown patterns using a teaching signals that is
generated using the decision of the classifier ensemble, i.e. the
classifiers self-supervise each other. Experiments on a set of object
images are presented. Our experiments investigate different classifier
models, different fusing techniques, different training sizes and
different input features. Experimental results reveal that the proposed
self-supervised ensemble learning approach reduces classification
error over the single classifier and the traditional ensemble classifier
approachs.