Supervised learning: Classification

Research output: Chapter in Book/Report/Conference proceedingEntry for encyclopedia/dictionary

Abstract

Supervised learning is the task of building a model that is able to fit the available observations. In the area of supervised learning, classification is one of the most studied problems. Given a set of predefined class labels (two or more) and a set of available observations, the aim is to build a model based on the features of the observations that is able to assign each observation to the corresponding class. In Bioinformatics several problems can be formulated as a classification task. This article introduces several supervised learning techniques that are commonly used to address a classification problem, presenting the most used measures to evaluate the performance of a classification model.

Original languageEnglish
Title of host publicationEncyclopedia of Bioinformatics and Computational Biology
Subtitle of host publicationABC of Bioinformatics
EditorsShoba Ranganathan, Michael Gribskov, Kenta Nakai, Christian Schönbach
PublisherElsevier
Pages342-349
Number of pages8
Volume1-3
ISBN (Electronic)9780128114322
ISBN (Print)9780128114148
DOIs
Publication statusPublished - 2019

Keywords

  • Bioinformatics
  • Classification
  • Machine learning
  • Supervised learning

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