Towards robust classifiers using optimal rule discovery

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.