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Towards robust classifiers using optimal rule discovery

机译:使用最佳规则发现实现鲁棒分类器

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A classification rule set is usually generated from history data to make predictions on future coming data that is usually not as complete as the training data. In this work, we provide a review of the robust rule-based optimal associate classifier (OAC) and its main building blocks. OAC is robust in the sense that it is able to make an accurate prediction when the future record is incomplete. OAC robustness is achieved by finding a larger classification rule set. We propose to initially transform the database to an item set tree (IST) data structure for efficient support-counting. Then, the optimal rule discovery (ORD) is adopted to mine the rules that are fed to OAC to select the classification rules from. Several experiments have been conducted to compare OAC classification accuracy and number of rules for a wide range of settings, and a classifier measure is introduced.
机译:通常从历史数据中生成分类规则集,以对将来的数据进行预测,而这些数据通常不如训练数据那么完整。在这项工作中,我们提供了对基于规则的鲁棒的最佳关联分类器(OAC)及其主要构建块的回顾。在将来的记录不完整时,OAC可以做出准确的预测,因此它很健壮。 OAC鲁棒性是通过找到更大的分类规则集来实现的。我们建议最初将数据库转换为项目集树(IST)数据结构,以进行有效的支持计数。然后,采用最佳规则发现(ORD)来挖掘输入到OAC的规则,以从中选择分类规则。已经进行了一些实验,以比较OAC分类准确度和各种设置下的规则数量,并引入了分类器度量。

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