Semi-supervised incremental learning with few examples for discovering medical association rules.

ABSTRACT:

Background

Association Rules are one of the main ways to represent structural patterns underlying raw data. They represent dependencies between sets of observations contained in the data. The associations established by these rules are very useful in the medical domain, for example in the predictive health field. Classic algorithms for association rule mining give rise to huge amounts of possible rules that should be filtered in order to select those most likely to be true. Most of the proposed techniques for these tasks are unsupervised. However, the accuracy provided by unsupervised systems is limited. Conversely, resorting to annotated data for training supervised systems is expensive and time-consuming. The purpose of this research is to design a new semi-supervised algorithm t

SUBMITTER: Sanchez-de-Madariaga R 

PROVIDER: S-EPMC8785547 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature