Eager to be Lazy: Towards a Complexity-guided Textual Case-Based Reasoning System

Finding an ideal representation for a case-base is important for a CBR system. This choice of an ideal representation is guided by the complexity of the cases. Based on the needs of each individual case, richer features are used for representation if required. While the framework is fairly general, this paper demonstrates its effectiveness on text classification due to the ease of evaluation. Each test case is treated differently by the classifier, in that if a shallow representation is deemed adequate for assigning a class label, the algorithm does away with a richer representation which is computationally expensive to generate. We also provided a time-budgeted evaluation of our framework which suggests that it holds promise in minimizing redundant or misleading comparisons and minimize time without compromising on effectiveness.