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CT < DT >: Extending the application of the consolidation methodology even further
Author(s) -
Ibarguren Igor,
Pérez Jesús M.,
Muguerza Javier,
Gurrutxaga Ibai
Publication year - 2017
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12212
Subject(s) - chaid , computer science , consolidation (business) , machine learning , artificial intelligence , classifier (uml) , data mining , algorithm , decision tree , accounting , business
The consolidation process, originally applied to the C4.5 tree induction algorithm, improved its discriminating capacity and stability. Consolidation creates multiple samples and builds a simple (nonmultiple) classifier by applying the ensemble process during the model construction phase. The work presented in this paper aims to show the consolidation process can improve algorithms other than C4.5 by applying the consolidation process to three tree induction algorithms: a variant of the chi‐squared automatic interaction detector (CHAID*), C4.4, and CHAIC (also a contribution of this paper). The consolidation of CHAID* and CHAIC, required solving the handicap of consolidating the value groupings proposed by each CHAID* or CHAIC tree for discrete attributes. The experimentation is divided in 3 classification contexts for 96 datasets. Results show that consolidated algorithms perform robustly, ranking competitively in all contexts, never falling into lower positions unlike most of the other rule‐induction algorithms considered in the study. When performing a global comparison consolidated algorithms rank in top positions.