Title: Incremental Transductive Learning Approaches to Schistosomiasis Vector Classification,
Abstract: The key issues pertaining to collection of epidemic disease data for our
analysis purposes are that it is a labour intensive, time consuming and
expensive process resulting in availability of sparse sample data which we use
to develop prediction models. To address this sparse data issue, we present
novel Incremental Transductive methods to circumvent the data collection
process by applying previously acquired data to provide consistent,
confidence-based labelling alternatives to field survey research. We
investigated various reasoning approaches for semisupervised machine learning
including Bayesian models for labelling data. The results show that using the
proposed methods, we can label instances of data with a class of vector density
at a high level of confidence. By applying the Liberal and Strict Training
Approaches, we provide a labelling and classification alternative to standalone
algorithms. The methods in this paper are components in the process of reducing
the proliferation of the Schistosomiasis disease and its effects.