Reusable components in decision tree induction algorithms

  • Authors:

  • Milija Suknovic;Boris Delibasic;Milos Jovanovic;Milan Vukicevic;Dragana Becejski-Vujaklija;Zoran Obradovic

  • Affiliations:

  • University of Belgrade, Faculty of Organizational Sciences, Jove Ilica 154, Belgrade, Serbia;University of Belgrade, Faculty of Organizational Sciences, Jove Ilica 154, Belgrade, Serbia;University of Belgrade, Faculty of Organizational Sciences, Jove Ilica 154, Belgrade, Serbia;University of Belgrade, Faculty of Organizational Sciences, Jove Ilica 154, Belgrade, Serbia;University of Belgrade, Faculty of Organizational Sciences, Jove Ilica 154, Belgrade, Serbia;Temple University, Information Science and Technology Center, 19122, Philadelphia, PA, USA

  • Venue:
  • Computational Statistics
  • Year:
  • 2012

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Abstract

We propose a generic decision tree framework that supports reusable components design. The proposed generic decision tree framework consists of several sub-problems which were recognized by analyzing well-known decision tree induction algorithms, namely ID3, C4.5, CART, CHAID, QUEST, GUIDE, CRUISE, and CTREE. We identified reusable components in these algorithms as well as in several of their partial improvements that can be used as solutions for sub-problems in the generic decision tree framework. The identified components can now be used outside the algorithm they originate from. Combining reusable components allows the replication of original algorithms, their modification but also the creation of new decision tree induction algorithms. Every original algorithm can outperform other algorithms under specific conditions but can also perform poorly when these conditions change. Reusable components allow exchanging of solutions from various algorithms and fast design of new algorithms. We offer a generic framework for component-based algorithms design that enhances understanding, testing and usability of decision tree algorithm parts.