Name
Abstract | ||
|---|---|---|
The goal of this study was to develop an algorithm for n-group classification by non-parametric linear discriminant analysis using genetic algorithms. This algorithm tested on both theoretical and real-world data. This approach has an advantage over parametric methods in that it does not depend on knowing or being able to predict the distribution parameters of the objects being classified. It was found that this genetic algorithm, using linear decision functions and a hierarchical binary classification, is capable of classification of instances into n groups where n is greater than two. The method not only appears to work but it also appears to be able to identify the hierarchy inherent in the classification structure. |
Year | DOI | Venue |
|---|---|---|
1994 | 10.1145/326619.326725 | SAC |
Keywords | Field | DocType |
linear discriminant functions,genetic algorithms,genetic algorithm,classification,n-group classification,binary classification | Optimal discriminant analysis,Pattern recognition,Computer science,Multiple discriminant analysis,Kernel Fisher discriminant analysis,N-group (finite group theory),Artificial intelligence,Linear discriminant analysis,Statistical classification,Genetic algorithm,Machine learning | Conference |
ISBN | Citations | PageRank |
0-89791-647-6 | 2 | 0.69 |
References | Authors | |
7 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aaron H. Konstam | 1 | 9 | 4.56 |