Hybrid genetic algorithm for dual selection |
| |
Authors: | Frederic Ros Serge Guillaume Marco Pintore Jacques R. Chrétien |
| |
Affiliation: | (1) GEMALTO, avenue de la Pomme de Pin, St. Cyr en Val, 45060 Orléans Cedex, France;(2) Cemagref, 34000 Montpellier, France;(3) BioChemics Consulting, 16 rue Leonard de Vinci, 45074 Orléans Cedex 2, France |
| |
Abstract: | In this paper, a hybrid genetic approach is proposed to solve the problem of designing a subdatabase of the original one with the highest classification performances, the lowest number of features and the highest number of patterns. The method can simultaneously treat the double problem of editing instance patterns and selecting features as a single optimization problem, and therefore aims at providing a better level of information. The search is optimized by dividing the algorithm into self-controlled phases managed by a combination of pure genetic process and dedicated local approaches. Different heuristics such as an adapted chromosome structure and evolutionary memory are introduced to promote diversity and elitism in the genetic population. They particularly facilitate the resolution of real applications in the chemometric field presenting databases with large feature sizes and medium cardinalities. The study focuses on the double objective of enhancing the reliability of results while reducing the time consumed by combining genetic exploration and a local approach in such a way that excessive computational CPU costs are avoided. The usefulness of the method is demonstrated with artificial and real data and its performance is compared to other approaches. |
| |
Keywords: | Feature selection Genetic algorithm Heuristics Classification k-nearest neighbor method |
本文献已被 SpringerLink 等数据库收录! |
|