Pham Viet Anh^1,3, Nguyen Ngoc Thuy⁴, Nguyen Long Giang², Pham Dinh Khanh⁵, Nguyen The Thuy^1,6,*

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2971-2988, 2023, DOI:10.32604/csse.2023.042068

Abstract Attribute reduction, also known as feature selection, for decision information systems is one of the most pivotal issues in machine learning and data mining. Approaches based on the rough set theory and some extensions were proved to be efficient for dealing with the problem of attribute reduction. Unfortunately, the intuitionistic fuzzy sets based methods have not received much interest, while these methods are well-known as a very powerful approach to noisy decision tables, i.e., data tables with the low initial classification accuracy. Therefore, this paper provides a novel incremental attribute reduction method to deal more effectively with noisy decision tables,… More >

Changshun Liu¹, Yan Liu¹, Jingjing Song^1,*, Taihua Xu^1,2

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2059-2075, 2023, DOI:10.32604/iasc.2023.039592

Abstract Attribute reduction, as one of the essential applications of the rough set, has attracted extensive attention from scholars. Information granulation is a key step of attribute reduction, and its efficiency has a significant impact on the overall efficiency of attribute reduction. The information granulation of the existing neighborhood rough set models is usually a single layer, and the construction of each information granule needs to search all the samples in the universe, which is inefficient. To fill such gap, a new neighborhood rough set model is proposed, which aims to improve the efficiency of attribute reduction by means of two-layer… More >

Tianshun Xing, Jianjun Chen^*, Taihua Xu, Yan Fan

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 561-581, 2023, DOI:10.32604/iasc.2023.037874

Abstract It is well-known that attribute reduction is a crucial action of rough set. The significant characteristic of attribute reduction is that it can reduce the dimensions of data with clear semantic explanations. Normally, the learning performance of attributes in derived reduct is much more crucial. Since related measures of rough set dominate the whole process of identifying qualified attributes and deriving reduct, those measures may have a direct impact on the performance of selected attributes in reduct. However, most previous researches about attribute reduction take measures related to either supervised perspective or unsupervised perspective, which are insufficient to identify attributes… More >

Mohsen Eid¹, Tamer Medhat^2,*, Manal E. Ali³

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1531-1544, 2023, DOI:10.32604/csse.2023.031745

Abstract An information system is a type of knowledge representation, and attribute reduction is crucial in big data, machine learning, data mining, and intelligent systems. There are several ways for solving attribute reduction problems, but they all require a common categorization. The selection of features in most scientific studies is a challenge for the researcher. When working with huge datasets, selecting all available attributes is not an option because it frequently complicates the study and decreases performance. On the other side, neglecting some attributes might jeopardize data accuracy. In this case, rough set theory provides a useful approach for identifying superfluous… More >

Lina Wang^1,2,*, Qixiang Zhang¹, Xiling Niu¹, Yongjun Ren³, Jinyue Xia⁴

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1765-1781, 2021, DOI:10.32604/cmc.2021.017516

Abstract Outlier detection is a key research area in data mining technologies, as outlier detection can identify data inconsistent within a data set. Outlier detection aims to find an abnormal data size from a large data size and has been applied in many fields including fraud detection, network intrusion detection, disaster prediction, medical diagnosis, public security, and image processing. While outlier detection has been widely applied in real systems, its effectiveness is challenged by higher dimensions and redundant data attributes, leading to detection errors and complicated calculations. The prevalence of mixed data is a current issue for outlier detection algorithms. An… More >