Distributed Methodologies for Imbalanced Classification Problems: Parameter Analysis and Tuning

Imbalanced classification problems represent a current challenge in data mining research, due to the classifiers' inability to produce sufficiently good models in such situations. We have previously proposed a general methodology for improving the performance of classifiers under imbalance conditions: ECSB -- Evolutionary Cost-Sensitive Balancing. This paper provides an empirical analysis on a distributed approach for ECSB (dECSB). The influence of the number of splits on the quality of the output classification model is studied on several data sets and J4.8 as base classifier. The data sets have been partitioned according to the imbalance ratio and the instances per attributes ratio. We found that the appropriate number of splits is highly dependent on the problem at hand, however, an influence of the two imbalance-related factors is present. The effect of altering the genetic settings has also been investigated, in the attempt to identify several values which constantly yield good results. Again, we found the results to be highly dependent on the problem, with some data sets exhibiting low performance variations due to the genetic settings.