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Economic Granularity Interval in Decision Tree Algorithm Standardization from an Open Innovation Perspective: Towards a Platform for Sustainable Matching

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journal contribution
posted on 10.02.2022, 16:54 by Tao Li, Lei Ma, Zheng LiuZheng Liu, Kaitong Liang
In the context of the application of artificial intelligence in an intellectual property trading platform, the number of demanders and suppliers that exchange scarce resources is growing continuously. Improvement of computational power promotes matching efficiency significantly. It is necessary to greatly reduce energy consumption in order to realize the machine learning process in terminals and microprocessors in edge computing (smart phones, wearable devices, automobiles, IoT devices, etc.) and reduce the resource burden of data centers. Machine learning algorithms generated in an open community lack standardization in practice, and hence require open innovation participation to reduce computing cost, shorten algorithm running time, and improve human-machine collaborative competitiveness. The purpose of this study was to find an economic range of the granularity in a decision tree, a popular machine learning algorithm. This work addresses the research questions of what the economic tree depth interval is and what the corresponding time cost is with increasing granularity given the number of matches. This study also aimed to balance the efficiency and cost via simulation. Results show that the benefit of decreasing the tree search depth brought by the increased evaluation granularity is not linear, which means that, in a given number of candidate matches, the granularity has a definite and relatively economical range. The selection of specific evaluation granularity in this range can obtain a smaller tree depth and avoid the occurrence of low efficiency, which is the excessive increase in the time cost. Hence, the standardization of an AI algorithm is applicable to edge computing scenarios, such as an intellectual property trading platform. The economic granularity interval can not only save computing resource costs but also save AI decision-making time and avoid human decision-maker time cost.

History

Published in

Journal of Open Innovation: Technology, Market, and Complexity

Publisher

MDPI

Version

VoR (Version of Record)

Citation

Li, T.; Ma, L.; Liu, Z.; Liang, K. (2020) 'Economic Granularity Interval in Decision Tree Algorithm Standardization from an Open Innovation Perspective: Towards a Platform for Sustainable Matching', Journal of Open Innovation: Technology, Market, and Complexity 6, 149. https://doi.org/10.3390/joitmc6040149

Electronic ISSN

2199-8531

Cardiff Met Affiliation

  • Cardiff School of Management

Cardiff Met Authors

Zheng Liu

Cardiff Met Research Centre/Group

  • Welsh Centre for Business and Management Research

Copyright Holder

© The Authors

Language

en