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A Particle Swarm Optimization Approach for the Case Retrieval Stage in CBR

机译:CBR案例检索阶段的粒子群优化方法

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Finding the good experiment to reuse from the case memory is the key of success in Case Based Reasoning (CBR). The paper presents a novel associative memory model to perform this task. The algorithm is founded on a Particle Swarm Optimization (PSO) approach to compute the neighborhood of a new problem. Then, direct access to the cases in the neighborhood is performed. The model was experimented on the Adult dataset, acquired from the University of California at Irvine Machine Learning Repository and compared to flat memory model for performance. The obtained results are very promising.
机译:在基于案例的推理(CBR)中,找到从案例存储器中重用的良好实验是成功的关键(CBR)。本文提出了一种新的关联内存模型来执行此任务。该算法建立在粒子群优化(PSO)方法上,以计算新问题的邻域。然后,执行对邻域中的情况的直接访问。该模型在成人数据集上进行了实验,从加利福尼亚大学获得了Irvine机器学习存储库,并与扁平内存模型进行了比较的性能。获得的结果非常有前途。

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