| Grant number: | 09/12963-2 |
| Support Opportunities: | Scholarships in Brazil - Master |
| Start date: | March 01, 2010 |
| End date: | December 31, 2010 |
| Field of knowledge: | Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques |
| Principal Investigator: | Ana Carolina Lorena |
| Grantee: | Newton Spolaôr |
| Host Institution: | Centro de Matemática, Computação e Cognição (CMCC). Universidade Federal do ABC (UFABC). Santo André , SP, Brazil |
Abstract The occurrence of irrelevant and/or redundant attributes in databases may impair the performance of computational processes for knowledge discovery, which motivates the application of feature selection techniques. The combinatorial nature of this problem makes the use of heuristics methods such as genetic algorithms appropriate, in order to obtain or approximate optimal subset of attributes.In many applications of feature selection one wants to optimize conflicting goals, such as the predictive performance of a subset of attributes and the cardinality of that subset. These characteristics enable the formulation of the feature selection task as a multiobjective optimization problem.This project aims to study and propose a method involving the use of multiobjective genetic algorithms in the feature selection problem, given the recent progresses that have been achieved in correlate state-of-art work. (AU) | |
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