Journal
EVOLVING SYSTEMS
Volume 8, Issue 1, Pages 3-18Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s12530-016-9159-3
Keywords
Naive Bayes classifier; Pattern recognition; Classification accuracy; Labeled/unlabeled data; Local decision metrics
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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 na < ve 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.
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