Discovering useful concept prototypes for classification based on filtering and abstraction

The nearest-neighbor algorithm and its derivatives have been shown to perform well for pattern classification. Despite their high classification accuracy, they suffer from high storage requirement, computational cost, and sensitivity to noise. We develop anew framework, called ICPL (Integrated Concept Prototype Learner), which integrates instance-filtering and instance-abstraction techniques by maintaining a balance of different kinds of concept prototypes according to instance locality. The abstraction component, based on typicality, employed in our ICPL framework is specially designed for concept integration. We have conducted experiments on a total of 50 real-world benchmark data sets. We find that our ICPL framework maintains or achieves better classification accuracy and gains a significant improvement in data reduction compared with existing filtering and abstraction techniques as well as some existing techniques.