RT Journal Article
T1 Classification of high dimensional data using LASSO ensembles
A1 Urda, Daniel
A1 Franco, Leonardo
A1 Jerez-Aragonés, José Manuel
K1 Bioinformática
AB The estimation of multivariable predictors with good performance  in  high  dimensional  settings  is  a  crucial  task  in biomedical  contexts.  Usually,  solutions  based  on  the  applicationof  a  single  machine  learning  model  are  provided  while  the  use of ensemble methods is often overlooked within this area despitethe  well-known  benefits  that  these  methods  provide  in  terms  of predictive performance. In this paper, four ensemble approaches are  described  using  LASSO  base  learners  to  predict  the  vital status  of  a  patient  from  RNA-Seq  gene  expression  data.  The results  of  the  analysis  carried  out  in  a  public  breast  invasive cancer  (BRCA)  dataset  shows  that  the  ensemble  approaches outperform  statistically  significant  the  standard  LASSO  modelconsidered  as  baseline  case.  We  also  perform  an  analysis  of the  computational  costs  involved  for  each  of  the  approaches,providing   different   usage   recommendations   according   to   the available  computational  power.
LK http://hdl.handle.net/10630/14831
UL http://hdl.handle.net/10630/14831
LA eng
NO Urda, D., Franco, L. and Jerez, J.M. (2017). Classification of high dimensional data using LASSO ensembles. Proceedings IEEE SSCI'17, Symposium Series on Computational Intelligence, Honolulu, Hawaii, U.S.A. (2017). ISBN: 978-1-5386-2726-6
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 10 abr 2026