Assessing the performance of a swarm-based biclustering technique for data imputation

Although the missing data problem has been studied for many years, it is still a relevant and challenging problem nowadays. Data can be missing for a variety of reasons, and there are several techniques capable of processing missing data. A parcel of them tries to estimate the missing values. This technique is called imputation. Recently, it was proposed a biclustering algorithm, based on Swarm Intelligence, named SwarmBCluster, to impute missing data. As it is a novel and promising algorithm, this paper intends to investigate the influence of its parameters on the performance. To achieve this objective, this paper will compare SwarmBCluster with other two imputation algorithms and, after that, it will perform a sensitivity analysis. The quality of the imputations is measured with the Root Mean Squared Error (RMSE). The experiments showed that SwarmBCluster presents good results concerning the RMSE metric and that the proper choice of parameters can considerably improve the performance of the algorithm.