A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules

Our goal in this paper is to automatically extract a set of decision rules (rule set) that best explains a classification data set. First, a large set of decision rules is extracted from a set of decision trees trained on the data set. The rule set should be concise, accurate, have a maximum coverage and minimum number of inconsistencies. This problem can be formalized as a modified version of the weighted budgeted maximum coverage problem, known to be NP-hard. To solve the combinatorial optimization problem efficiently, we introduce a nested genetic algorithm which we then use to derive explanations for ten public data sets

Badenian and Sarmatian s.str. from the Carpathian area: Overview and ongoing research on Hungarian and Romanian small vertebrate evolution

© 2016 Académie des sciencesThe fossil record from the Carpathian area plays a key role for the understanding of the processes leading to the faunal interchanges between western Europe and Asia Minor during the late part of the Middle Miocene. Important mammal successions are now available from the Central Paratethys, especially Hungary and Romania. Here, we present the current state-of-the-art of the ongoing research concerning these faunas, especially small mammals and herpetofauna. We underscore the relevance of the Middle to earliest Late Miocene fossil record from these countries for chrono(bio)stratigraphic and palaeoenvironmental studies at the Eurasian scale