Semi-supervised algorithms are well-known for their ability to combine both supervised and unsupervised strategies for optimizing their learning ability under the assumption that only a few examples together with their full feature set are given. In such cases, the use of weak learners as base classifiers is usually preferred, since the iterative behavior of semi-supervised schemes require the building of new temporal models during each new iteration. Locally weighted naive Bayes classifier is such a classifier that encompasses the power of NB and k-NN algorithms. In this work, we have implemented a self-labeled weighted variant of local learner which uses NB as the base classifier of self-training scheme. We performed an in depth comparison with other well-known semi-supervised classification methods on standard benchmark datasets and we reached to the conclusion that the presented technique had better accuracy in most cases.
['Stamatis Karlos', 'Nikos Fazakis', 'Angeliki-Panagiota Panagopoulou', 'Sotiris B. Kotsiantis', 'Kyriakos N. Sgarbas']
Locally application of naive Bayes for self-training