Theory combination: an alternative to data combination
Kai Ming Ting, Boon Toh Low · 1996
The approach of combining theories learned from multiple batches of data provide an alternative to the common practice of learning one theory from all the available data (i.e., the data combination approach). This paper empirically examines the base-line behaviour of the theory combination approach in classification tasks. We find that theory combination can lead to better performance even if the disjoint batches of data are drawn randomly from a larger sample, and relate the relative performance of the two approaches to the learning curve of the classifier used. The practical implication of our results is that one should consider using theory combination rather than data combination, especially when multiple batches of data for the same task are readily available. Another interesting result is that we empirically show that the near-asymptotic performance of a single theory, in some classification task, can be significantly improved by combining multiple theories (of the same algorithm...