A Semi-Supervised Multi-view Genetic Algorithm
Gergana Lazarova, Ivan Koychev · 2014
Semi-supervised learning combines labeled and unlabeled examples in order to find better future predictions. Usually, in this area of research we have massive amounts of unlabeled instances and few labeled ones. In this paper each instance has attributes from multiple sources of information (views) and a genetic algorithm is applied for regression function learning. Based on the few labeled examples and the agreement among the views on the unlabeled examples the error of the algorithm is optimized, striving after minimal regularized risk. The performance of the algorithm (based on RMSE: root-mean-square error), is compared to its supervised equivalent and shows very good results.