Embedded local feature selection within mixture of experts

Billy Peralta, Alvaro Soto

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)


A useful strategy to deal with complex classification scenarios is the "divide and conquer" approach. The mixture of experts (MoE) technique makes use of this strategy by jointly training a set of classifiers, or experts, that are specialized in different regions of the input space. A global model, or gate function, complements the experts by learning a function that weighs their relevance in different parts of the input space. Local feature selection appears as an attractive alternative to improve the specialization of experts and gate function, particularly, in the case of high dimensional data. In general, subsets of dimensions, or subspaces, are usually more appropriate to classify instances located in different regions of the input space. Accordingly, this work contributes with a regularized variant of MoE that incorporates an embedded process for local feature selection using L1 regularization. Experiments using artificial and real-world datasets provide evidence that the proposed method improves the classical MoE technique, in terms of accuracy and sparseness of the solution. Furthermore, our results indicate that the advantages of the proposed technique increase with the dimensionality of the data.

Original languageEnglish
Pages (from-to)176-187
Number of pages12
JournalInformation Sciences
Publication statusPublished - 10 Jun 2014


  • Embedded feature selection
  • Local feature selection
  • Mixture of experts
  • Regularization

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence


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