Paper: A Spatial EA Framework for Parallelizing Machine Learning Methods
Abstract: The scalability of machine learning (ML) algorithms has become increasingly important due to the ever increasing size of datasets and increasing complexity of the models induced. Standard approaches for dealing with this issue generally involve developing parallel and distributed versions of the ML algorithms and/or reducing the dataset sizes via sampling techniques. In this paper we describe an alternative approach that combines features of spatially-structured evolutionary algorithms (SSEAs) with the well-known machine learning techniques of ensemble learning and boosting. The result is a powerful and robust framework for parallelizing ML methods in a way that does not require changes to the ML methods. We fi rst describe the framework and illustrate its behavior on a simple synthetic problem, and then evaluate its scalability and robustness using several diff rent ML methods on a set of benchmark problems from the UC Irvine ML database.

Source Code for All Experiments
Many of the WEKA algorithms have bugs when run with multi-threaded code (IBK, nearest neighbor code for example), hence we implemented two different packages. One, complete single-threaded WEKA classifier, that can work with explorer and experimenter in generic way. Only thing is while running the experiments, seed have to be manually changed. The parallel algorithm uses the "time in milliseconds" as the large seed for getting the validation set from the training set. Having a large seed is important for getting different sample of the data to train and validate when running multiple runs of the algorithm.

                Parameters when running through WEKA (note the useWeightedAUCForSelection which uses the validation set to get AUC instead of just % correct is used)