Evolutionary approach for automated component-based decision tree algorithm design

Authors:
Miloš Jovanović;Boris Delibašić;Milan Vukićević;Milija Suknović;Milan Martić
Affiliations:
Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia;Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia;Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia;Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia;Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia
Venue:
Intelligent Data Analysis - Business Analytics and Intelligent Optimization
Year:
2014

Citing 32
Cited 0

A Distance-Based Attribute Selection Measure for Decision Tree Induction

Machine Learning
C4.5: programs for machine learning

C4.5: programs for machine learning
Design patterns: elements of reusable object-oriented software

Design patterns: elements of reusable object-oriented software
Where does reuse start?

ACM SIGSOFT Software Engineering Notes
Finding the Right Hybrid Algorithm – A Combinatorial Meta-Problem

Annals of Mathematics and Artificial Intelligence
Induction of Decision Trees

Machine Learning
GA Tree: genetically evolved decision trees

ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
A hybrid decision tree/genetic algorithm method for data mining

Information Sciences: an International Journal - Special issue: Soft computing data mining
Particle swarm based Data Mining Algorithms for classification tasks

Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
Toward Intelligent Assistance for a Data Mining Process: An Ontology-Based Approach for Cost-Sensitive Classification

IEEE Transactions on Knowledge and Data Engineering
YALE: rapid prototyping for complex data mining tasks

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Selecting representative examples and attributes by a genetic algorithm

Intelligent Data Analysis
The Need for Open Source Software in Machine Learning

The Journal of Machine Learning Research
Top 10 algorithms in data mining

Knowledge and Information Systems
The lack of a priori distinctions between learning algorithms

Neural Computation
A framework for constructing complete algorithms based on local search

AI Communications - Constraint Programming for Planning and Scheduling
Parameter determination of support vector machine and feature selection using simulated annealing approach

Applied Soft Computing
Cross-disciplinary perspectives on meta-learning for algorithm selection

ACM Computing Surveys (CSUR)
LEGAL-tree: a lexicographic multi-objective genetic algorithm for decision tree induction

Proceedings of the 2009 ACM symposium on Applied Computing
Ant colony and particle swarm optimization for financial classification problems

Expert Systems with Applications: An International Journal
Cost-sensitive classification: empirical evaluation of a hybrid genetic decision tree induction algorithm

Journal of Artificial Intelligence Research
Reusable components for partitioning clustering algorithms

Artificial Intelligence Review
Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach

Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach
Ant colony based rule generation for reusable software component retrieval

ACM SIGSOFT Software Engineering Notes
Evolutionary data analysis for the class imbalance problem

Intelligent Data Analysis
Parameters optimization of support vector machine based on simulated annealing and genetic algorithm

ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Using datamining techniques to help metaheuristics: a short survey

HM'06 Proceedings of the Third international conference on Hybrid Metaheuristics
Reusable components in decision tree induction algorithms

Computational Statistics
Experiment databases: a novel methodology for experimental research

KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Data mining with an ant colony optimization algorithm

IEEE Transactions on Evolutionary Computation
Component-based decision trees for classification

Intelligent Data Analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a framework for automated design of component-based decision tree algorithms. These algorithms are being constructed by interchanging components extracted from decision tree algorithms and their partial improvements. Manual selection of the best-suited algorithm for a specific problem is a complex task because of the huge algorithmic space derived from component-based design. The proposed framework searches through the algorithmic space with an evolutionary algorithm by interchanging components and tuning parameters, and finds a near optimal algorithm for a specific problem. Through experiments we show that using this meta-heuristic is justified in automated component-based algorithm design. This approach is useful not only as an algorithm design help, but also as a technology enhanced learning tool, which aids the understanding of the algorithms.