IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v385y2007i1p199-207.html
   My bibliography  Save this article

Subspace ensembles for classification

Author

Listed:
  • Sun, Shiliang
  • Zhang, Changshui

Abstract

Ensemble learning constitutes one of the principal current directions in machine learning and data mining. In this paper, we explore subspace ensembles for classification by manipulating different feature subspaces. Commencing with the nature of ensemble efficacy, we probe into the microcosmic meaning of ensemble diversity, and propose to use region partitioning and region weighting to implement effective subspace ensembles. Individual classifiers possessing eminent performance on a partitioned region reflected by high neighborhood accuracies are deemed to contribute largely to this region, and are assigned large weights in determining the labels of instances in this area. A robust algorithm “Sena” that incarnates the mechanism is presented, which is insensitive to the number of nearest neighbors chosen to calculate neighborhood accuracies. The algorithm exhibits improved performance over the well-known ensembles of bagging, AdaBoost and random subspace. The difference of its effectivity with varying base classifiers is also investigated.

Suggested Citation

  • Sun, Shiliang & Zhang, Changshui, 2007. "Subspace ensembles for classification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 385(1), pages 199-207.
    • Handle: RePEc:eee:phsmap:v:385:y:2007:i:1:p:199-207
    DOI: 10.1016/j.physa.2007.05.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437107005304
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2007.05.010?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:385:y:2007:i:1:p:199-207. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.