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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hayashi, Y. Fujisawa, S. |
| Copyright Year | 2015 |
| Description | Author affiliation: Dept. of Comput. Sci., Meiji Univ., Kawasaki, Japan (Hayashi, Y.; Fujisawa, S.) |
| Abstract | In this paper, we review all our work since 2012 and propose a strategic approach for the Multiple-MLP Ensemble Re- RX algorithm. We first describe the background and procedures of the Recursive-Rule Extraction (Re-RX) algorithm family and its variants, including the Multiple-MLP Ensemble Re-RX algorithm (“Multiple-MLP Ensemble”), which uses the Re-RX algorithm as its core. The proposed strategic approach consists of two processes: non-pruning for the trained neural network ensembles without continuous attributes and a relaxed rule generation scheme using continuous attributes to extract extremely accurate, comprehensible, and concise rules for multi-class mixed datasets (i.e., discrete attributes and continuous attributes). We conducted experiments to find rules for seven kinds of multi-class mixed datasets and compared the accuracy, comprehensibility, and conciseness for the Multiple-MLP Ensemble Re-RX algorithm. The strategic approach for the Multiple-MLP Ensemble Re-RX algorithm outperformed the original Multiple-MLP Ensemble Re- RX algorithm. These results confirm that the strategic approach for the Multiple-MLP Ensemble algorithm facilitates the migration from existing data systems toward new accurate analytic systems and Big Data. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 495325 |
| Page Count | 8 |
| File Format | |
| ISSN | 21614407 |
| e-ISBN | 9781479919604 |
| DOI | 10.1109/IJCNN.2015.7280387 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-07-12 |
| Publisher Place | Ireland |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Classification algorithms Biological neural networks Data mining Neurons Relaxed rule generation Scheme Re-RX Algorithm Rule Extraction MLP Data Mining Big Data Neural Network Ensemble Mixed Dataset Ensemble Concept Non-pruning |
| Content Type | Text |
| Resource Type | Article |
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