Locally application of naive Bayes for self-training

• Published in: Evolving systems Vol. 8; no. 1; pp. 3 - 18
• Main Authors: Karlos, Stamatis, Fazakis, Nikos, Panagopoulou, Angeliki-Panagiota, Kotsiantis, Sotiris, Sgarbas, Kyriakos
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2017
• Subjects: Artificial Intelligence; Complex Systems; Complexity; Engineering; Original Paper; Pattern recognition; Naive Bayes classifier; Local decision metrics; Classification accuracy; Labeled/unlabeled data
• ISSN: 1868-6478; 1868-6486
• DOI: 10.1007/s12530-016-9159-3

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 naïve 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.