A Multiobjective Genetic Algorithm for Feature Selection in Data Mining
The rapid advance of computer based high-throughput technique have provided unparalleled opportunities for humans to expand capabilities in production, services, communications, and research. Meanwhile, immense quantities of high-dimensional data are accumulated challenging state-of-the-art data mining techniques. The intelligent analysis of Databases may be affected by the presence of unimportant features, which motivates the application of Feature Selection. By treating this task as a search and optimization process, it is possible to use the synergy between Genetic Algorithms and Multi-objective Optimization to carry out the search for (quasi) optimal subsets of features considering possible conflicting importance criteria.