In article number 1901124, Tian‐Ling Ren and co‐workers comprehensively review wearable electronics based on two‐dimensional materials including graphene, transition‐metal dichalcogenides (TMDs), and transition metal carbides or carbonitrides (MXenes). Due to their extraordinary physiochemical properties, they have displayed promising applications in various physiological information detection.
Classification is an important machine learning problem, and decision tree construction algorithms are an important class of solutions to this problem. RainForest is a scalable way to implement decision tree construction algorithms. It consists of several algorithms, of which the best one is a hybrid between a traditional recursive implementation and an iterative implementation which uses more memory but involves less write operations. We propose an optimized algorithm inspired by RainForest. By using a more sophisticated switching criterion between the two algorithms, we are able to get a performance gain even when all statistical information fits in memory. Evaluations show that our method can achieve a performance boost of 2.8 times in average than the traditional recursive implementation.
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