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The purpose of this paper is to define the Internet as a virtual space supported by technologies and presented in the form of socioeconomic relations from the perspective of political economy. The Internet space is a unique virtual commodity different from ordinary commodities and has the following effect characteristics: super replicability, space- and time-transcendence, open-source shareability and reality–virtuality transformation.

Internet space can also be imagined as a piece of virtual land. Internet space can be deemed as a piece of virtual land and its value can be divided into labor value and virtual value. The pricing model of virtual value is mainly determined by the gain and discount rate and this value comes from the transfer and markup of social value. In the context of the Internet Plus era, Internet space has become an essential economic factor that influences human economic activities.

Therefore, it is of practical significance and theoretical value to introduce Internet space as an economic variable into the framework of economic theory. The realistic logic of Internet space is to influence human economic behaviors with the combination of information binding.

The theoretical mechanism is to have an impact on the micro-market price by changing market relations from two-dimensional to three-dimensional. Its path to functioning at the macro level is to influence economic behaviors by changing the expectations of investment and consumption, resulting in new economic trends.

The purpose of this paper is to build a compact and accurate classifier for high-dimensional classification.

A classification approach based on class-dependent feature subspace (CFS) is proposed. CFS is a class-dependent integration of a support vector machine (SVM) classifier and associated discriminative features. For each class, our genetic algorithm (GA)-based approach evolves the best subset of discriminative features and SVM classifier simultaneously. To guarantee convergence and efficiency, the authors customize the GA in terms of encoding strategy, fitness evaluation, and genetic operators.

Experimental studies demonstrated that the proposed CFS-based approach is superior to other state-of-the-art classification algorithms on UCI data sets in terms of both concise interpretation and predictive power for high-dimensional data.

UCI data sets rather than real industrial data are used to evaluate the proposed approach. In addition, only single-label classification is addressed in the study.

The proposed method not only constructs an accurate classification model but also obtains a compact combination of discriminative features. It is helpful for business makers to get a concise understanding of the high-dimensional data.

The authors propose a compact and effective classification approach for high-dimensional data. Instead of the same feature subset for all the classes, the proposed CFS-based approach obtains the optimal subset of discriminative feature and SVM classifier for each class. The proposed approach enhances both interpretability and predictive power for high-dimensional data.