Finding the Real Differences Between Learning Algorithms.

In the process of selecting a machine learning algorithm to solve a problem, questions like the following commonly arise: (1) Are some algorithms basically the same, or are they fundamentally different? (2) How different? (3) How do we measure that difference? (4) If we want to combine algorithms, what algorithms and combinators should be tried? This research proposes COD (Classifier Output Difference) distance as a diversity metric. COD separates difference from accuracy, COD goes beyond accuracy to consider differences in output behavior as the basis for comparison. The paper extends earlier on COD by giving a basic comparison to other diversity metrics, and by giving an example of using COD data as a predictive model from which to select algorithms to include in an ensemble. COD may fill a niche in metalearning as a predictive aid to selecting algorithms for ensembles and hybrid systems by providing a simple, straightforward, computationally reasonable alternative to other approaches. [ABSTRACT FROM AUTHOR]