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Enhanced Random Forest Algorithms for Partially Monotone Ordinal Classification
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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)
doi:10.1609/aaai.v33i01.33013224
fatcat:wdto7wj635c7xdx4lyi4iwnntu