Predictive learning with structured (grouped) data

Lichen Liang, Feng Cai, Vladimir Cherkassky

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


Many applications of machine learning involve sparse and heterogeneous data. For example, estimation of diagnostic models using patients' data from clinical studies requires effective integration of genetic, clinical and demographic data. Typically all heterogeneous inputs are properly encoded and mapped onto a single feature vector, used for estimating a classifier. This approach, known as standard inductive learning, is used in most application studies. Recently, several new learning methodologies have emerged. For instance, when training data can be naturally separated into several groups (or structured), we can view model estimation for each group as a separate task, leading to a Multi-Task Learning framework. Similarly, a setting where the training data are structured, but the objective is to estimate a single predictive model (for all groups), leads to the Learning with Structured Data and SVM+ methodology recently proposed by Vapnik [(2006). Empirical inference science afterword of 2006. Springer]. This paper describes a biomedical application of these new data modeling approaches for modeling heterogeneous data using several medical data sets. The characteristics of group variables are analyzed. Our comparisons demonstrate the advantages and limitations of these new approaches, relative to standard inductive SVM classifiers.

Original languageEnglish (US)
Pages (from-to)766-773
Number of pages8
JournalNeural Networks
Issue number5-6
StatePublished - Jul 2009

Bibliographical note

Funding Information:
This work was supported, in part, by NSF grant ECCS-0802056, by the A. Richard Newton Breakthrough Research Award from Microsoft Research, and by the BICB grant from the University of Minnesota, Rochester.

Copyright 2009 Elsevier B.V., All rights reserved.


  • Heterogeneous data
  • Learning with structured data
  • Model selection
  • Multi-task learning
  • SVM
  • SVM-Plus


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