In the realm of data mining, several key issues exists in the traditional classification algorithms, such as low readability, large rule number, and low accuracy with information losing. In this paper, we propose a new classification methodology, called fault tolerance XCS in integer (FTXI), by extending XCS to handle conditions in integers and integrating the mechanism of fault tolerance in the context of data mining into the framework of XCS. We also design and generate appropriate artificial data sets for examining and verifying the proposed method. Our experiments indicate that FTXI can provide the least rule number, obtain high prediction accuracy, and offer rule readability, compared to C4.5 and XCS in integer without fault tolerance.
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