Book ChapterDOI
The Construction and Evaluation of Decision Trees: a Comparison of Evolutionary and Concept Learning Methods
H. Kennedy,C. Chinniah,P. V. G. Bradbeer,L. Morss +3 more
- pp 147-162
TLDR
The CALTROP program is presented, which provides a test of the feasibility of representing a decision tree as a linear chromosome and applying a genetic algorithm to the optimisation of the decision tree with respect to the classification of test sets of example data.Abstract:
The CALTROP program which is presented in this paper provides a test of the feasibility of representing a decision tree as a linear chromosome and applying a genetic algorithm to the optimisation of the decision tree with respect to the classification of test sets of example data. The unit of the genetic alphabet (the “caltrop”) is a 3-integer string corresponding to a subtree of the decision tree. The program offers a user a choice of mating strategies and mutation rates. Test runs with different data sets show that the decision trees produced by the CALTROP program usually compare favourably with those produced by the popular automatic induction algorithm, ID3.read more
Citations
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Journal ArticleDOI
Can genetic programming improve software effort estimation? A comparative evaluation
Colin J. Burgess,Martin Lefley +1 more
TL;DR: GP has the potential to be a valid additional tool for software effort estimation but set up and running effort is high and interpretation difficult, as it is for any complex meta-heuristic technique.
Journal ArticleDOI
A Survey of Evolutionary Algorithms for Decision-Tree Induction
TL;DR: This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction, which provides an up-to-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach.
Journal ArticleDOI
Operations research and data mining
TL;DR: A survey of the intersection of operations research and data mining is provided to illustrate the range of interactions between the two fields, present some detailed examples of important research work, and provide comprehensive references to other important work in the area.
Journal ArticleDOI
An investigation of machine learning based prediction systems
Carolyn Mair,Gada Kadoda,Martin Lefley,Keith Phalp,Chris Schofield,Martin Shepperd,Steve Webster +6 more
TL;DR: It is shown that ANN methods have superior accuracy and that RI methods are least accurate, however, this view is somewhat counteracted by problems with explanatory value and configurability.
Journal ArticleDOI
On the Use of Optimization for Data Mining: Theoretical Interactions and eCRM Opportunities
TL;DR: It is argued that the reformulation of eCRM problems within this new framework of analysis can result in more powerful analytical approaches.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Book
C4.5: Programs for Machine Learning
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Journal ArticleDOI
Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
Book ChapterDOI
A Comparative Analysis of Selection Schemes Used in Genetic Algorithms
David E. Goldberg,Kalyanmoy Deb +1 more
TL;DR: A number of selection schemes commonly used in modern genetic algorithms are compared on the basis of solutions to deterministic difference or differential equations, verified through computer simulations to provide convenient approximate or exact solutions and useful convergence time and growth ratio estimates.