id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-034846-05h2no14	Singer, Gonen	Ordinal Decision-Tree-Based Ensemble Approaches: The Case of Controlling the Daily Local Growth Rate of the COVID-19 Epidemic	2020-08-07		.txt	text/plain	7264	360	50	We demonstrate the applicability of the approaches using AdaBoost and random forest algorithms for the task of classifying the regional daily growth factor of the spread of an epidemic based on a variety of explanatory factors. We use the proposed measure to develop ordinal decision-tree-based ensemble approaches, i.e., ordinal AdaBoost and random forest models, which are known to outperform individual classifiers. The main objectives of this study are fourfold: (i) to extend the weighted information gain measure such that the classification error can be measured from a statistical value that is not necessarily defined by a single class; (ii) to develop ordinal decision-tree-based ensemble approaches in which an objective-based information gain measure is used; (iii) to examine the advantage of combining ordinal decision-tree-based ensemble approaches with non-ordinal individual classifiers to leverage the strengths of each type of classifier; and (iv) to examine the ability to carry out multi-class identification of different levels of a daily growth factor using ordinal decision-tree-based ensemble approaches.	./cache/cord-034846-05h2no14.txt	./txt/cord-034846-05h2no14.txt