Journal Article10.1109/TEVC.2003.819265
Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study
TL;DR: The results show that the evolutionary instance selection algorithms consistently outperform the nonevolutionary ones, the main advantages being: better instance reduction rates, higher classification accuracy, and models that are easier to interpret.
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Abstract: Evolutionary algorithms are adaptive methods based on natural evolution that may be used for search and optimization As data reduction in knowledge discovery in databases (KDDs) can be viewed as a search problem, it could be solved using evolutionary algorithms (EAs) In this paper, we have carried out an empirical study of the performance of four representative EA models in which we have taken into account two different instance selection perspectives, the prototype selection and the training set selection for data reduction in KDD This paper includes a comparison between these algorithms and other nonevolutionary instance selection algorithms The results show that the evolutionary instance selection algorithms consistently outperform the nonevolutionary ones, the main advantages being: better instance reduction rates, higher classification accuracy, and models that are easier to interpret
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Citations
•Book
Genetic Algorithms
David E. Goldberg,William Shakespeare +1 more
- 01 Jan 2002
TL;DR: The present work expresses the problem as a multi-objective optimization problem and a methodology has been proposed based on multi-objective genetic algo-rithm (MOGA) that exploits the effectiveness of MOGA for searching global optimal solutions in selecting an appropriate image enhancement operator.
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Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power
TL;DR: This paper focuses on the use of nonparametric statistical inference for analyzing the results obtained in an experiment design in the field of computational intelligence, and presents a case study which involves a set of techniques in classification tasks.
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KEEL: a software tool to assess evolutionary algorithms for data mining problems
Jesús Alcalá-Fdez,Luciano Sánchez,Salvador García,M. J. del Jesus,Sebastián Ventura,Josep Maria Garrell,José Otero,Cristóbal Romero,Jaume Bacardit,Víctor M. Rivas,Juan Carlos Fernández,Francisco Herrera +11 more
- 15 Oct 2008
TL;DR: KEEL as discussed by the authors is a software tool to assess evolutionary algorithms for data mining problems of various kinds including regression, classification, unsupervised learning, etc., which includes evolutionary learning algorithms based on different approaches: Pittsburgh, Michigan and IRL.
Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study
TL;DR: A taxonomy based on the main characteristics presented in prototype selection is proposed and an experimental study involving different sizes of data sets is conducted for measuring their performance in terms of accuracy, reduction capabilities, and runtime.
A review of instance selection methods
TL;DR: This work is focused on presenting a survey of the main instance selection methods reported in the literature, and shows how the training set is reduced which allows reducing runtimes in the classification and/or training stages of classifiers.
424
References
Genetic algorithms in search, optimization and machine learning
David E. Goldberg
- 01 Jan 1989
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.
58.6K
•Book
Genetic algorithms in search, optimization, and machine learning
David E. Goldberg
- 01 Sep 1988
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.
•Book
Adaptation in natural and artificial systems
John H. Holland
- 01 Jan 1975
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
•Book
C4.5: Programs for Machine Learning
J. Ross Quinlan
- 15 Oct 1992
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.
27.2K
•Book
Genetic Algorithms
David E. Goldberg,William Shakespeare +1 more
- 01 Jan 2002
TL;DR: The present work expresses the problem as a multi-objective optimization problem and a methodology has been proposed based on multi-objective genetic algo-rithm (MOGA) that exploits the effectiveness of MOGA for searching global optimal solutions in selecting an appropriate image enhancement operator.
17.1K