The number of features that can be completed over an image is, for practical purposes, limitless. Unfortunately, the number of features that can be computed and exploited by most computer vision systems is considerably less. As a result, it is important to develop techniques for selecting features from very large data sets that include many irrelevant or redundant features. This work addresses the feature selection problem by proposing a three-step algorithm. The first step uses a variation of the well known Relief algorithm to remove irrelevance; the second step clusters features using K-means to remove redundancy; and the third step is a standard combinatorial feature selection algorithm. This three-step combination is shown to be more effective than standard feature selection algorithms for large data sets with lots of irrelevant and redundant features. It is also shown to he no worse than standard techniques for data sets that do not have these properties. Finally, we show a third experiment in which a data set with 4096 features is reduced to 5% of its original size with very little information loss.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Feature selection from huge feature sets


    Contributors:
    Bins, J. (author) / Draper, B.A. (author)


    Publication date :

    2001-01-01


    Size :

    790999 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Feature Selection from Huge Feature Sets

    Bins, J. / Draper, B. / IEEE | British Library Conference Proceedings | 2001


    FEATURE SUBSCRIPTIONS FOR MEDICAL DEVICE SYSTEM FEATURE SETS

    NEITZELL GRANT A / JEJURKAR ABHIJIT P / RAPALLINI LEONARDO et al. | European Patent Office | 2023

    Free access

    FEATURE SELECTION, CLUSTERING, AND PROTOTYPE PLACEMENT FOR TURBULENCE DATA SETS

    Barone, Matthew F. / Ray, Jaideep / Domino, Stefan | TIBKAT | 2021


    Feature Selection, Clustering, and Prototype Placement for Turbulence Data Sets

    Barone, Matthew F. / Ray, Jaideep / Domino, Stefan | AIAA | 2021


    AUTOMATIC VEHICLE FEATURE SELECTION

    PRZYBYLSKI MATTHEW G / ZINSER CRAIG H / CREEHAN JAMES SCOTT | European Patent Office | 2024

    Free access