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

Abstract

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.

Influence of Open Innovation Variables on the Competitive Edge of Small and Medium Enterprises

“…A challenge for SMEs is identifying how innovation will help them increase their competitiveness [22]. Through micro and macro-dynamics with a quadruple helix, it is possible to understand and control the complexity of open innovation [23,24]. Still, it is not easy to predict its competitive results [20], which can generate positive impacts, risks, and uncertainty [19].…”

Cultivating Regional Innovation Ecosystems in the Digital Age: Case Studies of Four Cites in China

“…First, it is digital-related innovation, a complex system composed of a digital innovator, data elements, users and other digital users to achieve open information sharing and cross-industry and cross-border cooperation and innovation (Helfat & Raubitschek, 2018; Izaskun et al, 2020; Zhang, 2021). It is to promote the innovative generation, application and diffusion of digital technologies to achieve value-added innovation (Adner & Kapoor, 2016) and to analyse the data by the Information and Communication Technology (ICT) industry to create digital products (Liu et al, 2020). Second, an innovation ecosystem means the optimisation and reconstruction of innovator behaviour using digital technologies (Xu et al, 2020).…”

“…Therefore, digitally empowered innovation ecosystems, in which digital processes and the innovator are deeply connected around a common core value proposition, enable innovation in the industry (Gupta et al, 2019; Zhang et al, 2021). Accordingly, an efficient way is to apply digital technologies in traditional industrial sectors (Liu et al, 2020).…”

“…Information exchange, user feedback and iterative renewal of innovation elements between innovators in the innovation ecosystem are carried out via innovation carriers. Additionally, the expansion of platform carriers drives the development of the innovation ecosystem (Kim & Kim, 2020; Li et al, 2020; Liang et al, 2022; Yun et al, 2022). The fourth component is digital governance, concerning how to design reasonable solutions to facilitate value co-creation (Teece, 2018).…”

Early Warning of Enterprise Financial Risk Based on Decision Tree Algorithm

“…In terms of the purpose and effect of safety event management, “plan ahead” is far better than “make up for the lost.” Li et al was the first scholar to use regression analysis to study financial risk early warning. The emergence of this research made discriminant analysis replaced by regression analysis and became the mainstream method in the field of financial crisis early warning in the 1980s [ 10 ]. Zhang et al referring to the evaluation method of Bank of America on the operation and financial status of small and medium-sized enterprises, sorted, and analyzed the reasons for the operation failure of small and medium-sized enterprises in Finland.…”