Classification based on Associations (CBA) - a performance analysis

Abstract

Classification Based on Associations (CBA) has for &nbsp;two decades been the algorithm of choice for researchers as well as &nbsp;practitioners owing to simplicity of the produced rules, accuracy of models, and also fast model building. &nbsp;Two versions of CBA differing in speed -- M1 and M2 -- were originally proposed &nbsp;by Liu et al in 1998. While the more complex M2 version was originally designated as on average 50% faster, in this article we present benchmarks performed with multiple CBA implementations on the UCI lymph dataset contesting the M2 supremacy: the results show that M1 had faster processing speeds in most evaluated setups. M2 was recorded to be faster only when the number of input rules was &nbsp;very small and the number of input instances was large. We hypothesize that the better performance of the &nbsp;M1 version can be attributed &nbsp;to &nbsp;recent advances in optimization of vectorized operations and memory structures in SciKit learn and R, which the M1 can better utilize due to better predispositions for vectorization.&nbsp;<br/>This paper is accompanied by a Python implementation of CBA available at https://pypi.org/project/pyARC/.