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| Adaptive Boundary Approximation Prototype Selection Algorithm |
| LI Juan1,2, WANG Yu-Ping1 |
1.School of Computer Science and Technology, Xidian University, Xi'an 710071
2.School of Distance Education, Shaanxi Normal University, Xi'an 710062 |
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Abstract The traditional prototype selection algorithms are susceptible to pattern reading sequence, abnormal patterns etc. Aiming at these problems, an improved prototype selection algorithm based on adaptive boundary approximation is proposed by a detailed analysis of the prototype learning rule. The prototype absorption strategy of condensed nearest neighbor algorithm (CNN) is improved and the closer homogeneous boundary prototype parallel to its current nearest one is retained. Meanwhile, the prototype updating strategy is built for achieving dynamic periodic updating to the prototype set. The proposed algorithm can overcome the above mentioned issues and effectively reduce the scale of prototype set. Experiments are made on the artificial dataset and UCI benchmark dataset, and the results show that the final prototype set obtained by the proposed algorithm reflects the distribution of the original dataset much better. It improves the average reduction ratio performance, has better classification accuracy and runs faster than other algorithms.
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Received: 13 November 2013
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2015, Vol. 28 
