A Tandem Evolutionary Algorithm for Identifying Causal Rules from Complex Data.

Evolutionary Computation
John P HanleyMargaret J Eppstein

Abstract

We propose a new evolutionary approach for discovering causal rules in complex classification problems from batch data. Key aspects include (a) the use of a hypergeometric probability mass function as a principled statistic for assessing fitness that quantifies the probability that the observed association between a given clause and target class is due to chance, taking into account the size of the dataset, the amount of missing data, and the distribution of outcome categories, (b) tandem age-layered evolutionary algorithms for evolving parsimonious archives of conjunctive clauses, and disjunctions of these conjunctions, each of which have probabilistically significant associations with outcome classes, and (c) separate archive bins for clauses of different orders, with dynamically adjusted order-specific thresholds. The method is validated on majority-on and multiplexer benchmark problems exhibiting various combinations of heterogeneity, epistasis, overlap, noise in class associations, missing data, extraneous features, and imbalanced classes. We also validate on a more realistic synthetic genome dataset with heterogeneity, epistasis, extraneous features, and noise. In all synthetic epistatic benchmarks, we consistently recove...Continue Reading

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Citations

Jun 12, 2020·The American Journal of Tropical Medicine and Hygiene·John P HanleyCarlota Monroy

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Methods Mentioned

BETA
feature array

Software Mentioned

DNFEA
ExSTraCS
CCEA
XCS

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