Decision forest is an ensemble classification method that combines multiple decision trees to in a manner that results in more accurate classifications. By combining multiple heterogeneous decision trees, decision forest is effective in mitigating noise that is often prevalent in real-world classifi
Quality criteria of genetic algorithms for construction of phylogenetic trees
β Scribed by Reijmers, T. H.; Wehrens, R.; Buydens, L. M. C.
- Publisher
- John Wiley and Sons
- Year
- 1999
- Tongue
- English
- Weight
- 226 KB
- Volume
- 20
- Category
- Article
- ISSN
- 0192-8651
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β¦ Synopsis
In this article the suitability of two different tree representations for the construction of phylogenetic trees with genetic algorithms is examined. On the one hand tree topologies are represented by means of a distance matrix while on the other hand the Prufer number tree representation is used. To assess αΊhe adequacy of both approaches a set of recently proposed quality criteria is used. The quality criteria can be used to monitor genetic algorithm approaches differing in configuration and setup, fitness function, or representation. In addition to the criteria for the repeatability of the optimization, criteria for the coverage of the search space are also used. On the basis of the optimization results of simulated data, the quality criteria show, in contrast to the error plots, a clear difference in the efficiency of both representations. It is concluded that the Prufer number representation yields a diverse set of good quality topologies αΊ hile the distance matrix representation mainly returns the optimal topology.
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