Combining accuracy and success-rate to improve the performance of eXtended Classifier System (XCS) for data-mining and control applications
Combining accuracy and success-rate to improve the performance of eXtended Classifier System (XCS) for data-mining and control applications
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DOI：
10.1016/j.engappai.2013.04.004
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发表时间：
2013-09
期刊：
影响因子：
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通讯作者：
M. Shariat Panahi;A. Yousefi;M. Khorshidi
中科院分区：
文献类型：
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作者：
M. Shariat Panahi;A. Yousefi;M. Khorshidi
扩展分类器系统（XCS）的出现提高了学习分类器系统的门槛，通过将LCS传统强化机制中的规则的准确性结合起来。然而，XCS及其扩展都没有考虑到分类器参与动作集的体验的本质。我们引入了一种经验评估机制，一旦将其添加到传统的XCS中，就会为动作集的每个成员分配一个成功率，表明分类器对系统对环境查询的正确响应的有效程度。将该增强系统应用于多个基准问题，结果表明该机制提高了增强系统的分类能力和收敛速度。应用结果表明，SRXCS在模式关联和模式识别任务上都有较好的表现。通过求解一个动态环境中相当复杂的自主移动机器人路径规划问题，进一步证明了该机制的适用性和有效性。
The emergence of eXtended Classifier Systems (XCS) raised the bar for Learning Classifier Systems by incorporating the accuracies of the rules in the LCS's traditional reinforcement mechanism. However, neither XCS nor its extensions take into account the nature of a classifier's experience of attending the action set. We introduce an experience-evaluation mechanism that, once added to the traditional XCS, would assigns to each member of the action set a success rate indicating how effectively the classifier has contributed to the correct responding of the system to the environment's queries. Application of the augmented system (called SRXCS) to several benchmark problems shows that the proposed mechanism enhances XCS' classification capability and its rate of convergence at the same time. Application results indicate that SRXCS performs notably better on both pattern association and pattern recognition tasks. The applicability and efficiency of the proposed mechanism is further demonstrated through solving a fairly complex path planning problem for an autonomous mobile robot in a dynamic environment.