Machine Learning with the Hierarchy-of-Hypotheses (HoH) Approach Discovers Novel Pattern in Studies on Biological Invasions

Autor/inn/en	Ryo, Masahiro; Jeschke, Jonathan M.; Rillig, Matthias C.; Heger, Tina
Titel	Machine Learning with the Hierarchy-of-Hypotheses (HoH) Approach Discovers Novel Pattern in Studies on Biological Invasions
Quelle	In: Research Synthesis Methods, 11 (2020) 1, S.66-73 (8 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Ryo, Masahiro)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1759-2879
DOI	10.1002/jrsm.1363
Schlagwörter	Artificial Intelligence; Case Studies; Biology; Research Reports; Classification; Computer Software; Evidence; Decision Making; Specialists; Mathematical Models; Meta Analysis + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Case study; Fallstudie; Case Study; Biologie; Research report; Forschungsbericht; Classification system; Klassifikation; Klassifikationssystem; Evidenz; Decision-making; Entscheidungsfindung; Mathematical model; Mathematisches Modell; Meta-analysis; Metaanalyse
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01