关键词: 数据交易平台, 高质量数据, K-medoids-NCA-SMOTE-BSVM, 多模型集成

Abstract: As an emerging factor of production, the demand for trading and circulation of data resources has shown explosive growth. The problem of data quality has sparked widespread concern along with the exponential growth of data scale, and a lot of low-quality data is flooding into different types of data resource trade platforms. How to identify high-quality data resources in the massive resources has become the key for data trading platforms to gain competitive advantages and improve the efficiency of factor allocation. Existing research has provided a basis for high-quality data identification in the platform trading context, but there are still two deficiencies: Firstly, it is challenging to meet the requirements for large-scale data resources’quality identification because the existing identification methods, which are only applicable to the quality evaluation of small-scale and homogeneous data resources, have more manual participation components and insufficient automation. Secondly, the existing identification methods ignore the problem of uneven distribution of data resources of different quality, which easily triggers the bias of classification results and is difficult to meet the robustness requirements of heterogeneous sample classification. This paper is to clarify the mechanism of high-quality data resource formation in the context of platform transactions, discover the key factors for high-quality data resource formation in the context of platform transactions, and propose a method for efficiently recognizing high-quality data from large-scale and heterogeneous data resources.
Data circulation and transaction are necessary for the realization of the value of data resources, and in a platform economy, data circulation is in the form of an open market with numerous participants. This paper studies the flow of data resources and the generation process of high-quality data within the platform environment and builds a high-quality data resource identification index system of “intrinsic quality-commodity characterization”. After that, it suggests a K-medoids-NCA-SMOTE-BSVM fusion model, handles the identification of high-quality data as a pattern recognition problem, and uses supervised machine learning to identify high-quality data and solve the issue. Four primary sections make up the model: (1)Synthesizing the number of views, collections and downloads of data resources as the basis of discrimination, using K-medoids to cluster the samples of data resources, automatically creating the classification labels for data resources, and calculating the ideal number of classification labels by combining them with profile coefficients. (2)The built metrics are downscaled using Nearest Neighbor Component Analysis (NCA) to come up with a new set of features, taking into account the possibility that the chosen metrics contain elements that are less important for the categorization of high-quality data resources and may therefore influence the model’s effectiveness. (3)After clustering division, the number of samples from different classes fluctuates widely, and therefore, to maintain the balance of data between classes, the few class oversampling method (SMOTE) is used to increase the amount of data from the few sample classes under the new feature set. (4)A nonlinear high-quality data resource identification model based on Bayesian optimization support vector machine (BSVM) is constructed, and it achieves classification prediction of high, medium, and low quality data of various calibers by using the data resource identification indexes after feature dimensionality reduction, the clustered data resources as input, labeling the clustered data resources with categories, and balancing the dataset as the model’s output. Finally, based on the API datasets of real data trading platform, Python is used to crawl the request parameters, return parameters, update frequency, data sources, data descriptions, labels, application scenarios, specifications, registered capital of service merchants, views, downloads, and favorites of data resources to carry out the empirical research.
The results show that: a)SMOTE balanced processing can improve the effect of data resource quality identification and improve the classification accuracy of the optimization model based on the comparison of unbalanced with balanced datasets. b) Whether based on imbalanced or balanced datasets, BSVM outperforms SVM, WOA-SVM, PSO-SVM, MLP, and CNN approaches in terms of prediction accuracy, and BSVM has higher algorithmic efficacy with less training time than other optimization algorithms. In summary, this paper, which is an innovative attempt and a significant addition to the theory of data resource quality assessment, clarifies the meaning of high-quality data resources, builds a high-quality data resource identification index system, and fully verifies the validity of the index system with the aid of trading platform data. It also builds a high-quality data resource identification model, which can effectively generate the quality labels of massively parallel data sets. It has significant guiding relevance for encouraging the active trading of data resources and can effectively develop quality labels for vast data resources. It can also increase the recognition accuracy of heterogeneous data resources.

Key words: data trading platform, high quality data, K-medoids-NCA-SMOTE-BSVM, multi model integration