A High Accurate Multiple Classifier System for Entity Resolution Using Resampling and Ensemble Selection

Classifiers are often used in entity resolution to classify record pairs into matches, nonmatches, and possible matches, the performance of classifiers is directly related to the performance of entity resolution. In this paper, we develop a multiple classifier system using resampling and ensemble selection. We make full use of the characteristics of entity resolution to distinguish ambiguous instances before classification, so that the algorithm can focus on the ambiguous instances in parallel. Instead of developing an empirical optimal resampling ratio, we vary the ratio in a range to generate multiple resampled data. Further, we use the resampled data to train multiple classifiers and then use ensemble selection to select the best classifiers subset, which is also the best resampling ratio combination. Empirical study shows our method has a relatively high accuracy compared to other state-of-the-art multiple classifiers systems.