Paper Info
Title
Evolutionary Nonlinear Data Transformation for Visualization and Classification Tasks.
Abstract
In this paper we propose new approach in data set. dimensionality reduction. We use classical principal component. analysis transformation. Instead of rejecting features we generate new one by using nonlinear feature transformation. The values of transformation weights are changed evolutionary by using genetic algorithms. Results show better classification rates in smaller feature space. Visualization results also look better.
Year
Venue
Keywords
2013
Federated Conference on Computer Science and Information Systems
genetic algorithms,principal component analysis,feature extraction,data reduction,data visualisation
Field
DocType
ISSN
Data mining,Dimensionality reduction,Computer science,Artificial intelligence,Genetic algorithm,k-nearest neighbors algorithm,Feature vector,Data visualization,Pattern recognition,Visualization,Feature extraction,Machine learning,Principal component analysis
Conference
2325-0348
Citations 
PageRank 
References 
0
0.34
1
Authors
1
Name
Order
Citations
PageRank
Kamil Zabkiewicz100.34