Name
Abstract | ||
|---|---|---|
An approach being explored to improve the usefulness of machine learning techniques for generating classification rules for complex, real-world data is described. The approach involves the use of genetic algorithms as a front end to a traditional rule induction system in order to identify and select the best subset of features to be used by the rule induction system. This approach has been implemented and tested on difficult texture classification problems. The results are encouraging and indicate that there are significant advantages to the approach in this domain |
Year | DOI | Venue |
|---|---|---|
1992 | 10.1109/TAI.1992.246402 | Arlington, VA |
Keywords | Field | DocType |
feature extraction,genetic algorithms,image recognition,image texture,learning (artificial intelligence),classification rules,feature selection,genetic algorithms,machine learning,real-world data,rule induction system,texture classification problems | Front and back ends,Data mining,Feature selection,Computer science,Image processing,Artificial intelligence,Genetic algorithm,Algorithm design,Pattern recognition,Image texture,Feature extraction,Rule induction,Machine learning | Conference |
Citations | PageRank | References |
63 | 18.39 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haleh Vafaie | 1 | 312 | 52.81 |
| Kenneth De Jong | 2 | 3798 | 525.78 |