Obtaining labeled training examples for some classification tasks is often expensive, such as text classification, mail
filtering, while gathering large quantities of unlabeled examples is usually very cheap. Active learning aims at reducing
the number of training examples to be labeled by automatically processing the unlabeled examples, then selecting the
most informative ones with respect to a given cost function for a human to label. MBBNTree algorithm, which
integrates the advantage of Markov Blanket Bayesian Networks (MBBN) and Decision Tree, would behave better
performance than other Bayesian Networks for classification. But the available training samples with actual classes are
not enough for building MBBNTree classifier in practice. In this paper, the MBBNTree classifier algorithm based on the
Query-by-Committee of active learning would be presented to solve the problem of learning MBBNTree classifier from
unlabeled samples. Experimental results show that the proposed algorithm can reach the same accuracy as passive
learning with few labeled training examples.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.