Advances in Machine Learning for the Behavioral Sciences. (February 2020)

• Record Type: Journal Article
• Authors: Kliegr, Tomáš; Bahník, Štěpán; Fürnkranz, Johannes
• Other Names: Druckman Daniel guest-editor.; Donohue William guest-editor.
• Is Part Of: American behavioral scientist. Volume 64:Number 2(2020)
• Page Start: 145
• Page End: 175
• Publication Date: 2020-02
• Subjects: artificial intelligence -- big data -- descriptive data mining -- machine learning -- natural language processing -- text mining; Social sciences -- Periodicals; Political science -- Periodicals; United States -- Social conditions -- Periodicals; United States -- Politics and government -- Periodicals; 300
• DOI: 10.1177/0002764219859639
• Languages: English
• ISSNs: 0002-7642

Abstract

The areas of machine learning and knowledge discovery in databases have considerably matured in recent years. In this article, we briefly review recent developments as well as classical algorithms that stood the test of time. Our goal is to provide a general introduction into different tasks such as learning from tabular data, behavioral data, or textual data, with a particular focus on actual and potential applications in behavioral sciences. The supplemental appendix to the article also provides practical guidance for using the methods by pointing the reader to proven software implementations. The focus is on R, but we also cover some libraries in other programming languages as well as systems with easy-to-use graphical interfaces.