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Full-Text Articles in Human Ecology
A Tandem Evolutionary Algorithm For Identifying Causal Rules From Complex Data, John P. Hanley, Donna M. Rizzo, Jeffrey S. Buzas, Margaret J. Eppstein
A Tandem Evolutionary Algorithm For Identifying Causal Rules From Complex Data, John P. Hanley, Donna M. Rizzo, Jeffrey S. Buzas, Margaret J. Eppstein
College of Engineering and Mathematical Sciences Faculty Publications
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 …
Instagram Photos Reveal Predictive Markers Of Depression, Andrew G. Reece, Christopher M. Danforth
Instagram Photos Reveal Predictive Markers Of Depression, Andrew G. Reece, Christopher M. Danforth
College of Engineering and Mathematical Sciences Faculty Publications
Using Instagram data from 166 individuals, we applied machine learning tools to successfully identify markers of depression. Statistical features were computationally extracted from 43,950 participant Instagram photos, using color analysis, metadata components, and algorithmic face detection. Resulting models outperformed general practitioners’ average unassisted diagnostic success rate for depression. These results held even when the analysis was restricted to posts made before depressed individuals were first diagnosed. Human ratings of photo attributes (happy, sad, etc.) were weaker predictors of depression, and were uncorrelated with computationally-generated features. These results suggest new avenues for early screening and detection of mental illness.