Mining Data by Query-Based Error-Propagation

Neural networks have advantages of the high tolerance to noisy data as well as the ability to classify patterns having not been trained. While being applied in data mining, the time required to induce models from large data sets are one of the most important considerations. In this paper, we introduce a query-based learning scheme to improve neural networks' performance in data mining. Results show that the proposed algorithm can significantly reduce the training set cardinality. Additionally, the quality of training results can be also ensured. Our future work is to apply this concept to other data mining schemes and applications.