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| A Cascade Algorithm of Quantum Attribute Evolution Reduction and Classification Learning Based on Dynamic Crossover Cooperation |
| DING Wei-Ping1,2,3, WANG Jian-Dong1, GUAN Zhi-Jin2, SHI Quan2 |
1.College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016
2.School of Computer Science and Technology, Nantong University, Nantong 226019
3.Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou 215006 |
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Abstract Attribute reduction and rule classification learning are important contents for research and application of rough set theory. Taking advantage of quantum computing to accelerate the algorithm speed and co-searching of shuffled frog leaping algorithm, a cascade algorithm of attribute reduction and classification learning based on the dynamic quantum frog-leaping crossover cooperation is proposed. Individuals in the frog swarm are represented by multi-state gene qubits, and the dynamic adjustment strategy of quantum rotation angle is applied to accelerate its convergence. By the crossover coevolution mechanism, classification rules are extracted and reduced, and decision rule chains are introduced in the classification criterion of rough entropy thresholding. The double cascade model of attribute reduction and classification learning is constructed. Experimental simulations indicate the proposed algorithm has good performance for global optimization. Compared with other algorithms, it is more efficient on attribute reduction and rule classification learning.
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Received: 17 December 2010
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2011, Vol. 24 
