Search

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, attribute reduction of incomplete information system is studied. Firstly, optimistic and pessimistic generalized variable precision rough set models based on maximal consistent blocks are constructed. The relationship between the two models and their main properties are analyzed. After that β-lower optimistic (pessimistic) and β-upper distribution attribute reduction is defined, and the corresponding judgement theorem is given. Boolean method of attribute reduction is obtained, and it can keep the upper (lower) approximation distribution of the decision class unchanged. This method of constructing discernibility set between maximal consistent blocks reduces the size of the discernibility matrix, and the process of computing attribute reduction is simplified, which can effectively save computing time and storage space. Then two examples of incomplete information systems with “lost” “don't care” values and only “don't care” values are employed to illustrate the proposed method. Finally, 5 sets of incomplete information data sets from UCI data set are used to validate its effectiveness.

Combined with air material consumption features，this paper proposes a method combining the rough set（RS） and the support vector machine（SVM） for air material consumption prediction. Through attribute reduction of the incomplete information system of RS，redundant quantitative factors in the air material consumption information system are eliminated. On the basis of the importance of attributes，seven factors are reduced to three，and the core knowledge of the system is retained. After the introduction of SVM improved by the particle swarm optimization（PSO）model，this approach obtains the optimized parameter combination and establises the forecast model of air material consumption. The case study demonstrates that the prediction accuracy of the RS-PSO-SVM model is better than those of the PSO-SVM model or the RSback propagation（ BP）model. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the classification of incomplete information system, we improve the relationship of discernibility and perfect the tolerance relationship based on rough set theory, and propose an improved extended rough set model. To eliminate the noise in incomplete information system, we also introduced the variable precision rough set in the incomplete information system Finally, based on the improved discernibility relationship, this paper constructs an extended rough set model of variable precision whose feasibility and validity of the method are verified by empirical analysis. [ABSTRACT FROM AUTHOR]

In this paper, an extended rough set with a parameter in incomplete information system is proposed, which is called probability tolerance relation model based on probability similarity. The probability of the value of attributes is taken into account in this new model. Also the new model can classify data more precisely and improve the classification decision making by setting the parameter property. At last an example is given to demonstrate the feasibility and the advantage of the new model. [ABSTRACT FROM AUTHOR]

The classic rough sets theory is based on the equivalence relation and not used to applicate incomplete information system. In order to extend the rough sets theory, an improved rough set model based on valued tolerance relation and prior probability obtained out of incomplete information system was presented. As a part of the model, an attribute reduction algorithm applicated incomplete information system was proposed. This algorithm takes the null set as the beginning, does not need core calculating, can save time and space, and simplify the solving process as well. Finally, the model and the attribute reduction algorithm are verified under an incomplete information system. [ABSTRACT FROM AUTHOR]