Name
Abstract | ||
|---|---|---|
An appropriate pre-processing algorithm in classification is important and crucial with respect to classifier type. In this paper, two pre-processing methods are suggested to be applied before classification in order to increase classification accuracy. The aim of this approach is finding a transformation matrix to discriminate between classes by transforming data into the new space. In the first method, we use class independent LDA to increase classification accuracy. Because LDA cannot obtain optimal transformation, in the second approach, two evolutionary methods (Genetic Algorithm and Particle Swarm Optimization) are used to increase performance of LDA. The transformation matrix is independent of classifier and classifier type has no effect on computation of transformation matrix. Obtained results show that these pre-processing methods increase the accuracy of different classifiers. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1569901.1570230 | GECCO |
Keywords | Field | DocType |
pre-processing method,different classifier,evolutionary-class independent lda,genetic algorithm,appropriate pre-processing algorithm,classification accuracy,transformation matrix,obtained result,class independent lda,classifier type,optimal transformation,classification,evolutionary algorithm,particle swarm optimization | Particle swarm optimization,Evolutionary algorithm,Pattern recognition,Computer science,Artificial intelligence,Transformation matrix,Classifier (linguistics),Genetic algorithm,Machine learning,Computation | Conference |
Citations | PageRank | References |
3 | 0.47 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hossein Moeinzadeh | 1 | 23 | 4.07 |
| Mehdi Mohammadi | 2 | 1091 | 50.02 |
| Ahmad Akbari | 3 | 159 | 23.17 |
| Babak Nasersharif | 4 | 88 | 13.21 |