Open AccessJournal Article
Combining classification algorithms
52
TL;DR: Dissertacao de Doutoramento em Ciencia de Computadores apresentada a Faculdade de Ciencias da Universidade do Porto.
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Abstract: Dissertacao de Doutoramento em Ciencia de Computadores apresentada a Faculdade de Ciencias da Universidade do Porto
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A genetic algorithm-based rule extraction system
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Combining intelligent techniques for sensor fusion
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- 18 Nov 2002
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Empirical models based on machine learning techniques for determining approximate reliability expressions
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TL;DR: Two machine learning algorithms, decision trees and Hamming clustering, are compared in building approximate reliability expression (RE), and although both methods yield excellent predictions, the HC procedure achieves better results with respect to the DT algorithm.
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A rule induction approach to improve Monte Carlo system reliability assessment
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References
Prototype Selection for Composite Nearest Neighbor Classifiers
David Bingham Skalak
- 01 Sep 1995
TL;DR: Algorithms that combine a small number of component nearest neighbor classifiers, where each of the components stores aSmall number of prototypical instances are introduced, which yield composite classifiers that are more accurate than a nearest neighbors classifier that stores all training instances as prototypes.
Naive Bayesian Classifier Committees
Zijian Zheng
- 21 Apr 1998
TL;DR: Experiments show that this method significantly increases the prediction accuracy of the naive Bayesian classifier on average and performs better than the two approaches mentioned above in terms of higher prediction accuracy.
•Book
Predictive Data Mining: A Practical Guide
Sholom M. Weiss,Nitin Indurkhya +1 more
- 15 Aug 1997
TL;DR: This chapter discusses data mining, reduction and mining in the context of big data, and some of the lessons learned can be applied to other areas of science and engineering.
Semi-Naive Bayesian Classifier
Igor Kononenko
- 06 Mar 1991
TL;DR: The algorithm of the 'naive' Bayesian classifier (that assumes the independence of attributes) is extended to detect the dependencies between attributes to optimize the tradeoff between the 'non-naivety' and the reliability of approximations of probabilities.
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