Enhanced Random Forest Algorithms for Partially Monotone Ordinal Classification

Christopher Wayne Bartley, Wei Liu, Mark Reynolds

Research output: Chapter in Book/Conference paperConference paperpeer-review

10 Citations (Scopus)
1 Downloads (Pure)


One of the factors hindering the use of classification models in decision making is that their predictions may contradict expectations. In domains such as finance and medicine, the ability to include knowledge of monotone (nondecreasing) relationships is sought after to increase accuracy and user satisfaction. As one of the most successful classifiers, attempts have been made to do so for Random Forest. Ideally a solution would (a) maximise accuracy; (b) have low complexity and scale well; (c) guarantee global monotonicity; and (d) cater for multi-class. This paper first reviews the state-of-the-art from both the literature and statistical libraries, and identifies opportunities for improvement. A new rule-based method is then proposed, with a maximal accuracy variant and a faster approximate variant. Simulated and real datasets are then used to perform the most comprehensive ordinal classification benchmarking in the monotone forest literature. The proposed approaches are shown to reduce the bias induced by monotonisation and thereby improve accuracy. © 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org).
Original languageEnglish
Title of host publication33rd AAAI Conference on Artificial Intelligence (AAAI 2019)
Place of PublicationUSA
PublisherAAAI Press
Number of pages8
ISBN (Electronic)9781577358091
Publication statusPublished - 28 Jan 2019
Event33rd AAAI Conference on artificial intelligence 2019 - Hawaii, United States
Duration: 27 Jan 20191 Feb 2019


Conference33rd AAAI Conference on artificial intelligence 2019
Abbreviated titleAAAI-19
Country/TerritoryUnited States


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