A formula for multiple classifiers in data mining based on Brandt semigroups

Kelarev A., Yearwood J., MƏMMƏDOV M.

Semigroup Forum, vol.78, no.2, pp.293-309, 2009 (SCI-Expanded, Scopus) identifier

  • Nəşrin Növü: Article / Article
  • Cild: 78 Say: 2
  • Nəşr tarixi: 2009
  • Doi nömrəsi: 10.1007/s00233-008-9098-9
  • jurnalın adı: Semigroup Forum
  • Jurnalın baxıldığı indekslər: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Səhifə sayı: pp.293-309
  • Açar sözlər: Brandt semigroups, Classification, Data mining
  • Açıq Arxiv Kolleksiyası: Məqalə
  • Adres: Bəli

Qısa məlumat

A general approach to designing multiple classifiers represents them as a combination of several binary classifiers in order to enable correction of classification errors and increase reliability. This method is explained, for example, in Witten and Frank (Data Mining: Practical Machine Learning Tools and Techniques, 2005, Sect. 7.5). The aim of this paper is to investigate representations of this sort based on Brandt semigroups. We give a formula for the maximum number of errors of binary classifiers, which can be corrected by a multiple classifier of this type. Examples show that our formula does not carry over to larger classes of semigroups. © 2008 Springer Science+Business Media, LLC.