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DIMENSIONALITY REDUCTION FOR SENSORY DATASETS BASED ON MASTER-SLAVE SYNCHRONIZATION OF LORENZ SYSTEM.
- Source :
- International Journal of Bifurcation & Chaos in Applied Sciences & Engineering; May2013, Vol. 23 Issue 5, p-1, 15p, 1 Diagram, 1 Chart, 12 Graphs
- Publication Year :
- 2013
-
Abstract
- In this study, we propose a novel method for reducing the attributes of sensory datasets using Master-Slave Synchronization of chaotic Lorenz Systems (DPSMS). As part of the performance testing, three benchmark datasets and one Electronic Nose (EN) sensory dataset with 3 to 13 attributes were presented to our algorithm to be projected into two attributes. The DPSMS-processed datasets were then used as input vector to four artificial intelligence classifiers, namely Feed-Forward Artificial Neural Networks (FFANN), Multilayer Perceptron (MLP), Decision Tree (DT) and K-Nearest Neighbor (KNN). The performance of the classifiers was then evaluated using the original and reduced datasets. Classification rate of 94.5%, 89%, 94.5% and 82% were achieved when reduced Fishers iris, crab gender, breast cancer and electronic nose test datasets were presented to the above classifiers. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02181274
- Volume :
- 23
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- International Journal of Bifurcation & Chaos in Applied Sciences & Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 88006182
- Full Text :
- https://doi.org/10.1142/S0218127413300139