Abstract

Supervised learning techniques require large number of labeled examples to build a classifier which is often difficult and expensive to collect. Unsupervised learning techniques, even though do not require labeled examples often form clusters regardless of the intended purpose or context. The authors proposes a semi supervised learning framework that leverages the large number of unlabeled examples in addition to limited number of labeled examples to form clusters as per the context. This framework also supports the development of semi supervised classifier based on the proximity of unknown example to the clusters so formed. The authors proposes a new algorithm namely 201C;Semi Supervised Relevance Feature Estimation201D;, (SFRE), to identify the relevant features along with their significance weightages which is integrated with the proposed framework. Experiments conducted on the benchmark datasets from UCI gave results which are very promising and consistent even with lesser number of labeled examples.