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Multi-objective genetic programming for feature extraction and data visualization
- Source :
- Helvia. Repositorio Institucional de la Universidad de Córdoba, instname
- Publication Year :
- 2015
- Publisher :
- Springer Science and Business Media LLC, 2015.
-
Abstract
- Feature extraction transforms high dimensional data into a new subspace of lower dimensionalitywhile keeping the classification accuracy. Traditional algorithms do not consider the multi-objective nature of this task. Data transformations should improve the classification performance on the new subspace, as well as to facilitate data visualization, which has attracted increasing attention in recent years. Moreover, new challenges arising in data mining, such as the need to deal with imbalanced data sets call for new algorithms capable of handling this type of data. This paper presents a Pareto-basedmulti-objective genetic programming algorithm for feature extraction and data visualization. The algorithm is designed to obtain data transformations that optimize the classification and visualization performance both on balanced and imbalanced data. Six classification and visualization measures are identified as objectives to be optimized by the multi-objective algorithm. The algorithm is evaluated and compared to 11 well-known feature extraction methods, and to the performance on the original high dimensional data. Experimental results on 22 balanced and 20 imbalanced data sets show that it performs very well on both types of data, which is its significant advantage over existing feature extraction algorithms.
- Subjects :
- Computer science
Feature extraction
Genetic programming
Computational intelligence
02 engineering and technology
Machine learning
computer.software_genre
Theoretical Computer Science
Data visualization
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Visualization
business.industry
Classification
020201 artificial intelligence & image processing
Geometry and Topology
Data mining
Artificial intelligence
business
computer
Software
Subspace topology
Curse of dimensionality
Subjects
Details
- ISSN :
- 14337479 and 14327643
- Volume :
- 21
- Database :
- OpenAIRE
- Journal :
- Soft Computing
- Accession number :
- edsair.doi.dedup.....74c87584630b33056af8743445f4f346