PROPER: Performance visualization for optimizing and comparing ranking classifiers in MATLAB View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-12

AUTHORS

Samad Jahandideh, Fatemeh Sharifi, Lukasz Jaroszewski, Adam Godzik

ABSTRACT

BACKGROUND: One of the recent challenges of computational biology is development of new algorithms, tools and software to facilitate predictive modeling of big data generated by high-throughput technologies in biomedical research. RESULTS: To meet these demands we developed PROPER - a package for visual evaluation of ranking classifiers for biological big data mining studies in the MATLAB environment. CONCLUSION: PROPER is an efficient tool for optimization and comparison of ranking classifiers, providing over 20 different two- and three-dimensional performance curves. More... »

PAGES

15

References to SciGraph publications

  • 2001-10. Random Forests in MACHINE LEARNING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s13029-015-0047-1

    DOI

    http://dx.doi.org/10.1186/s13029-015-0047-1

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1029556476

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/26635892


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