A new similarity-based classifier with Dombi aggregative operators

Abstract

• A new classification method using Dombi aggregation operators is proposed and tested on real world datasets. • The most suitable Dombi operator for aggregation in each data set is determined. • The new classifier is efficient in classifying samples from both large and small datasets. • Classification accuracies obtained indicate an improvement compared to benchmark classifiers. In this paper we extend the similarity classifier to cover Dombi aggregation operators. The similarity classifier was earlier studied with the ordered weighted averaging (OWA) operator, the generalized mean, and other operators. We concentrate on the use of Dombi operators during aggregation of similarities within the classifier. Four Dombi aggregation operators applied here include: conjunctive, disjunctive, weighted conjunctive, and the product operator. From each of these operators, we form a variant of the similarity classifier. The proposed methods were tested on four real world medical datasets which include: fertility, lung cancer, Haberman’s survival, and liver disorder. The new classifiers achieved improved classification accuracies compared with earlier methods. Compared with the classifier using the generalized mean, the proposed method achieved an improvement of 17.80 % on fertility, 5.88 % on lung cancer, 0.65 % on Haberman’s survival, and 0.29 % on liver disorder datasets.