Advances in Machine Learning for the Behavioral Sciences

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.

References in corpus (12)

• Deep Learning in Neural Networks: An Overview
• Distributed Representations of Words and Phrases and their Compositionality
• Empirical Analysis of Predictive Algorithms for Collaborative Filtering
• Using Information Content to Evaluate Semantic Similarity in a Taxonomy
• Semantics derived automatically from language corpora contain human-like biases
• From Frequency to Meaning: Vector Space Models of Semantics
• Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition
• Word sense disambiguation: a survey
• Comparative study of LSA vs Word2vec embeddings in small corpora: a case study in dreams database
• MLIC: A MaxSAT-Based framework for learning interpretable classification rules
• WordNet-feelings: A linguistic categorisation of human feelings
• Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning