Exploring Decision Rules for Election Results by Classification Trees

Creative Commons License

Deveci Kocakoç İ., Köymen Keser İ.

Conference on Economies of the Balkan and Eastern European Countries, Bucharest, Romania, 10 - 12 May 2019, pp.107-115 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.18502/kss.v4i1.5982
  • City: Bucharest
  • Country: Romania
  • Page Numbers: pp.107-115
  • Keywords: classification trees, voting decision, C5.0 algorithm, decision trees
  • Dokuz Eylül University Affiliated: Yes


This study explores the most important socio-economic variables determining the voting decisions of the provinces in Municipality Elections by using classification trees. We collected data on many potential variables that may affect voting decisions in favor of a political party. Each province's economic, geographic and demographic data is taken into consideration as independent variables. The dependent variable is the winner party in 2014 Municipality Elections. Data set consists of 81 provinces' data on 69 variables. The aim of the study is to find which variables affect voting decision the most and try to find a pattern that may lead political campaigns. Amongst many classification algorithms, we used C5.0 algorithm coded in R. It helps us explore the structure of a set of data, while developing easy to visualize decision rules for predicting a categorical (classification tree) or continuous (regression tree) outcome. The C5.0 algorithm determines the separation criterion with the greatest information gain in each decision node and performs optimal separation.