Machine learning with the hierarchy-of-hypotheses (HoH) approach discovers novel pattern in studies on biological invasions
| Autor: | Tina Heger, Jonathan M. Jeschke, Matthias C. Rillig, Masahiro Ryo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
synthesis
Judgement computer.software_genre 01 natural sciences Machine Learning 010104 statistics & probability 0302 clinical medicine systematic review 030212 general & internal medicine Research Articles Hierarchy Ecology Geography Decision tree learning artificial intelligence ddc Research Design Meta-analysis Data Interpretation Statistical 500 Naturwissenschaften und Mathematik::500 Naturwissenschaften::507 Ausbildung Forschung verwandte Themen Algorithms Research Article Asia hierarchy‐of‐hypotheses approach Machine learning Education 03 medical and health sciences ddc:570 Animals Narrative 0101 mathematics Biology Ecosystem Institut für Biochemie und Biologie Structure (mathematical logic) Models Statistical business.industry Decision Trees hierarchy-of-hypotheses approach Reproducibility of Results Statistical model meta-analysis Review Literature as Topic meta‐analysis Artificial intelligence Evidence collection business Introduced Species computer |
| Zdroj: | Research Synthesis Methods |
| Popis: | Research synthesis on simple yet general hypotheses and ideas is challenging in scientific disciplines studying highly context-dependent systems such as medical, social, and biological sciences. This study shows that machine learning, equation-free statistical modeling of artificial intelligence, is a promising synthesis tool for discovering novel patterns and the source of controversy in a general hypothesis. We apply a decision tree algorithm, assuming that evidence from various contexts can be adequately integrated in a hierarchically nested structure. As a case study, we analyzed 163 articles that studied a prominent hypothesis in invasion biology, the enemy release hypothesis. We explored if any of the nine attributes that classify each study can differentiate conclusions as classification problem. Results corroborated that machine learning can be useful for research synthesis, as the algorithm could detect patterns that had been already focused in previous narrative reviews. Compared with the previous synthesis study that assessed the same evidence collection based on experts' judgement, the algorithm has newly proposed that the studies focusing on Asian regions mostly supported the hypothesis, suggesting that more detailed investigations in these regions can enhance our understanding of the hypothesis. We suggest that machine learning algorithms can be a promising synthesis tool especially where studies (a) reformulate a general hypothesis from different perspectives, (b) use different methods or variables, or (c) report insufficient information for conducting meta-analyses. Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe; 1171 |
| Databáze: | OpenAIRE |
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