Improvements on least squares twin multi-class classification support vector machine

Recently, least squares twin multi-class support vector machine (LSTKSVC) was proposed as a least squares version of twin multi-class classification support vector machine (Twin-KSVC), both based on twin support vector machine (TWSVM). In this paper, we propose a novel multi-class classifier termed...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Neurocomputing (Amsterdam) Ročník 313; s. 196 - 205
Hlavní autoři: de Lima, Márcio Dias, Costa, Nattane Luiza, Barbosa, Rommel
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 03.11.2018
Témata:
Least squares
Multi-class classification
Structural risk minimization
Twin support vector machine
Twin-KSVC
Multi-class classification
Twin-KSVC
Twin support vector machine
Least squares
Structural risk minimization
ISSN:0925-2312, 1872-8286
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Recently, least squares twin multi-class support vector machine (LSTKSVC) was proposed as a least squares version of twin multi-class classification support vector machine (Twin-KSVC), both based on twin support vector machine (TWSVM). In this paper, we propose a novel multi-class classifier termed as Improvements on least squares twin multi-class classification support vector machine that is motivated by LSTKSVC and Twin-KSVC. Similarly to LSTKSVC that evaluates all the training data into a ``1−versus−1−versus−rest″ structure, the algorithm here proposed generates ternary output {−1,0,+1}. Whereas Twin-KSVC needs to solve two quadratic programming problems (QPPs), the solution of the two modified primal problems for our algorithm is reduced to two systems of linear equations. Besides that, in our algorithm the structural risk minimization (SRM) principle is implemented by introducing a regularization term, along with minimizing the empirical risk. To test the efficacy and validity of the proposed method, numerical experiments on ten UCI benchmark data sets are performed. The results obtained further corroborate the effectiveness of the proposed algorithm.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2018.06.040