BEAGLE (Biological Evolutionary Algorithm Generating Logical Expressions) is a computer package for producing decision‐rules by induction from a database. It works on the principle of “Naturalistic Selection” whereby rules that fit the data badly are “killed off” and replaced by “mutations” of better rules or by new rules created by “mating” two better adapted rules. The rules are Boolean expressions represented by tree structures. The software consists of two Pascal programs, HERB (Heuristic Evolutionary Rule Breeder) and LEAF (Logical Evaluator And Forecaster). HERB improves a given starting set of rules by running over several simulated generations. LEAF uses the rules to classify samples from a database where the correct membership may not be known. Preliminary tests on three different databases have been carried out—on hospital admissions (classing heart patients as deaths or survivors), on athletic physique (classing Olympic finalists as long‐distance runners or sprinters) and on football results (categorizing games into draws and non‐draws). It appears from the tests that the method works better than the standard discriminant analysis technique based on a linear discriminant function, and hence that this long‐neglected approach warrants further investigation.
Structural and content-related deficits occur in connected discourse of patients with semantic dementia (SD). We used principal components analysis (PCA) to characterise the sources of variation in word usage during picture description by controls and SD patients. This data-driven approach allowed: comparison of the distance between individuals in the two-dimensional space; correlational analyses between principal component (PC) values and performance on other tests; identification of words whose variance contributed most to the definition of the PCs.Transcripts of Cookie Theft picture descriptions from 21 patients with SD and 21 controls were used to generate frequencies of all word types (n = 557) across participants. Frequency values of words with ≥10 occurrences (n= 81) were entered into a PCA. Values of emergent dimensions were correlated with performance on tests of single word meaning. The first PC accounted for 59% of the variance, the second for a further 10%. Patients and controls showed good separation within the resulting space. Factor loading scores indicated that control performance was characterised by function (factor 1) and content (factor 2) word usage, while patients showed a greater tendency to use pronouns, deictic and generic words. Knowledge of single word meaning correlated with factor 1 but not with factor 2. Differences in word usage can differentiate connected speech of SD patients from controls using a rapid, automated, data-driven algorithm. The distinction between groups, loadings on the two components, and their differential correlations with semantic tasks raise the possibility of independent differences in syntax and lexical content.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.