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Dimensionality reduction for sensory datasets based on master-slave synchronization of Lorenz system
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Ghaffari, Reza, Grosu, Ioan, Iliescu, Daciana, Hines, Evor and Leeson, Mark S. (2013) Dimensionality reduction for sensory datasets based on master-slave synchronization of Lorenz system. International Journal of Bifurcation and Chaos in Applied Sciences and Engineering, 23 (5). 1330013 . doi:10.1142/S0218127413300139
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Official URL: http://dx.doi.org/10.1142/S0218127413300139
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.
| Item Type: | Journal Article | ||||
|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) |
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| Divisions: | Faculty of Science > Engineering | ||||
| Library of Congress Subject Headings (LCSH): | Lorenz equations, Data mining , Big data | ||||
| Journal or Publication Title: | International Journal of Bifurcation and Chaos in Applied Sciences and Engineering | ||||
| Publisher: | World Scientific Publishing Co. Pte. Ltd. | ||||
| ISSN: | 0218-1274 | ||||
| Official Date: | May 2013 | ||||
| Dates: |
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| Volume: | 23 | ||||
| Number: | 5 | ||||
| Article Number: | 1330013 | ||||
| DOI: | 10.1142/S0218127413300139 | ||||
| Status: | Peer Reviewed | ||||
| Publication Status: | Published | ||||
| Access rights to Published version: | Restricted or Subscription Access |
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