ترجمه مقاله نقش ضروری ارتباطات 6G با چشم انداز صنعت 4.0
- مبلغ: ۸۶,۰۰۰ تومان
ترجمه مقاله پایداری توسعه شهری، تعدیل ساختار صنعتی و کارایی کاربری زمین
- مبلغ: ۹۱,۰۰۰ تومان
abstract
An information system as a database that represents relationships between objects and attributes is an important mathematical model. An interval-valued information system is a generalized model of single-valued information systems. As important evaluation tools in the field of machine learning, measures of uncertainty can quantify the dependence and similarity between two targets. However, the existing measures of uncertainty for intervalvalued information systems have not been thoroughly researched. This paper is devoted to the study of new measures of uncertainty for an interval-valued information system. Information structures are first introduced in a given interval-valued information system. Then, the dependence between two information structures is depicted. Next, new measures of uncertainty for an interval-valued information system are investigated by using the information structures. As an application of the proposed measures, the rough entropy of a rough set is proposed by means of information granulation. Finally, a numerical experiment on the Face recognition dataset is presented to demonstrate the feasibility of the proposed measures, and a statistical effectiveness analysis is conducted. The results are helpful for understanding the essence of uncertainty in interval-valued information systems.
Conclusions
In this paper, we have proposed four measuring tools (i.e., θ-information granulation, θ-information amount, θ-rough entropy and θ-information entropy) to evaluate the uncertainty of a given interval-valued information system by means of its information structure. As an application of information granulation, we have proposed the rough entropy of a rough set in interval-valued information systems. We have presented a numerical experiment on the Face recognition dataset and conducted a statistical effectiveness analysis from three aspects, namely, dispersion analysis, correlation analysis and variance analysis, to demonstrate the feasibility of the proposed measures. The measures proposed in this paper can be applied to data mining from interval data. In the future, we will consider additional applications of the proposed measures.