ACDC: $\alpha$-Carving Decision Chain for Risk Stratification

Autor:	Park, Yubin, Ho, Joyce, Ghosh, Joydeep
Rok vydání:	2016
Předmět:	Statistics - Machine Learning Computer Science - Learning
Druh dokumentu:	Working Paper
Popis:	In many healthcare settings, intuitive decision rules for risk stratification can help effective hospital resource allocation. This paper introduces a novel variant of decision tree algorithms that produces a chain of decisions, not a general tree. Our algorithm, $\alpha$-Carving Decision Chain (ACDC), sequentially carves out "pure" subsets of the majority class examples. The resulting chain of decision rules yields a pure subset of the minority class examples. Our approach is particularly effective in exploring large and class-imbalanced health datasets. Moreover, ACDC provides an interactive interpretation in conjunction with visual performance metrics such as Receiver Operating Characteristics curve and Lift chart. Comment: presented at 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016), New York, NY
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1606.05325 Zobrazit plný text záznamu View this record from Arxiv