Assessing the Performance of a Swarm-based Biclustering Technique for Data Imputation

by

Veroneze, R., Franca, F., D. and Zuben, F., J., V.

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Info: Proceedings of the 2011 IEEE Congress on Evolutionary Computation (Conference proceedings), 2011, p. 386-393
Keywords:Classification, clustering, data analysis, data mining, Data mining
Abstract:
Although the missing data problem [MD] 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. [MD] A parcel of them tries to estimate the missing values. [MV] This technique is called imputation. Recently, it was proposed a biclustering algorithm, based on Swarm Intelligence, [SI] named SwarmBCluster, to impute missing data. [MD] 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. [SA] 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.
Notes:
CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.
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BibTex:
@InProceedings{Veroneze:2011:AtPoaSBTfDI,
  title     = {Assessing the Performance of a Swarm-based Biclustering Technique for Data Imputation},
  author    = {Rosana Veroneze and Fabricio {de Franca} and Fernando J. {Von Zuben}},
  pages     = {386--393},
  booktitle = {Proceedings of the 2011 IEEE Congress on Evolutionary Computation},
  year      = {2011},
  editor = "Alice E. Smith",
  month     = {5-8 June},
  address   = {New Orleans, USA},
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  ISBN      = {0-7803-8515-2},
  keywords  = {Classification, clustering, data analysis and data mining, Data mining},
  abstract  = {
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.
},
  notes =	{CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET. },
}