3 years ago

One-class support vector classifiers: A survey

Shamshe Alam, Sanjay Kumar Sonbhadra, Sonali Agarwal, P. Nagabhushan

Over the past two decades, one-class classification (OCC) becomes very popular due to its diversified applicability in data mining and pattern recognition problems. Concerning to OCC, one-class support vector classifiers (OCSVCs) have been extensively studied and improved for the technology-driven applications; still, there is no comprehensive literature available to guide researchers for future exploration. This survey paper presents an up to date, structured and well-organised review on one-class support vector classifiers. This survey comprises available algorithms, parameter estimation techniques, feature selection strategies, sample reduction methodologies, workability in distributed environment and application domains related to OCSVCs. In this way, this paper offers a detailed overview to researchers looking for the state-of-the-art in this area.

Publisher URL: https://www.sciencedirect.com/science/article/pii/S0950705120301647

DOI: 10.1016/j.knosys.2020.105754

You might also like
Discover & Discuss Important Research

Keeping up-to-date with research can feel impossible, with papers being published faster than you'll ever be able to read them. That's where Researcher comes in: we're simplifying discovery and making important discussions happen. With over 19,000 sources, including peer-reviewed journals, preprints, blogs, universities, podcasts and Live events across 10 research areas, you'll never miss what's important to you. It's like social media, but better. Oh, and we should mention - it's free.

  • Download from Google Play
  • Download from App Store
  • Download from AppInChina

Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.