Name
Abstract | ||
|---|---|---|
This paper describes an approach being explored to improve the usefulness of machine learning techniques to classify complex, real world data. The approach involves the use of genetic algorithms as a "front end" to a traditional tree induction system (ID3) in order to find the best feature set to be used by the induction system. This approach has been implemented and tested on difficult texture classification problems. The results are encouraging and indicate significant advantages of the presented approach. |
Year | DOI | Venue |
|---|---|---|
1995 | 10.1109/TAI.1995.479372 | ICTAI |
Keywords | Field | DocType |
difficult texture classification problem,traditional tree induction system,real world data,genetic algorithm,induction system,genetic algorithms,restructuring feature space representations,significant advantage,front end,feature extraction,manufacturing,image texture,algorithm design and analysis,feature space,computer science,testing,machine learning,id3,image classification,image recognition,decision trees,feature selection,learning artificial intelligence | Decision tree,Feature vector,Pattern recognition,Feature selection,Computer science,Image texture,Feature extraction,Artificial intelligence,ID3,Contextual image classification,Genetic algorithm,Machine learning | Conference |
ISSN | ISBN | Citations |
1082-3409 | 0-8186-7312-5 | 20 |
PageRank | References | Authors |
1.47 | 3 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haleh Vafaie | 1 | 312 | 52.81 |
| Kenneth De Jong | 2 | 3798 | 525.78 |