Reusable components in decision tree induction algorithms

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,...

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Veröffentlicht in:Computational statistics Jg. 27; H. 1; S. 127 - 148
Hauptverfasser: Suknovic, Milija, Delibasic, Boris, Jovanovic, Milos, Vukicevic, Milan, Becejski-Vujaklija, Dragana, Obradovic, Zoran
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer-Verlag 01.03.2012
Springer Nature B.V
Schlagworte:
Algorithms
Carts
Decision trees
Economic Theory/Quantitative Economics/Mathematical Methods
Exchanging
Machine learning
Mathematical analysis
Mathematical models
Mathematics and Statistics
Open source software
Original Paper
Probability and Statistics in Computer Science
Probability Theory and Stochastic Processes
Replication
Reusable components
Statistics
Studies
Design
Decision tree
Algorithm
Generic
Framework
Reusable components
ISSN:0943-4062, 1613-9658
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
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ISSN:0943-4062
1613-9658
DOI:10.1007/s00180-011-0242-8