Incremental Transductive Learning Approaches to Schistosomiasis Vector Classification

TL;DR

This paper introduces incremental transductive learning methods to classify disease vectors with sparse data, reducing reliance on costly field surveys and improving prediction confidence in schistosomiasis control.

Contribution

It presents novel incremental transductive approaches, including Bayesian models, for semi-supervised classification of disease vectors using limited data.

Findings

High-confidence labeling of vector data achieved

Liberal and Strict Training Approaches improve classification accuracy

Methods offer a cost-effective alternative to field surveys

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.

Full text

See pages 1-last of ITAlncs.pdf