Feature Selection for Interval-Valued Data Based on D-S Evidence Theory

IEEE Access

Qinli Zhang¹  Yichun Peng² 

[1] School of Big Data and Artificial Intelligence, Chizhou University, Chizhou, China;School of Computer Science and Engineering, Yulin Normal University, Yulin, China;

关键词: Interval-valued data;    IVIS;    D-S evidence theory;    belief function;    plausibility function;    feature selection;   

DOI  :  10.1109/ACCESS.2021.3109013

来源: DOAJ

【 摘 要 】

Feature selection is one basic and critical technology for data mining, especially in current “big data era”. Rough set theory (RST) is sensitive to noise in feature selection due to the strict condition of equivalence relation. However, D-S evidence theory is flexible to measure uncertainty of information. This paper introduces robust feature evaluation metrics “belief function” and “plausibility function” into feature selection algorithm to avoid the defect that classification effect is affected by noise. First of all, similarity between information values in an interval-valued information system (IVIS) is given and a variable parameter to control the similarity of samples is introduced. Then, $\theta $ -lower approximation and $\theta $ -upper approximation in IVIS are put forward. Next, belief function and plausibility function based on $\theta $ -lower approximation and $\theta $ -upper approximation are put forward. Finally, four feature selection algorithms in an IVIS based on D-S evidence theory are proposed. The experimental results on four real interval-valued datasets show that the proposed metrics are robust to noise, and the proposed algorithms are more effective than the existing algorithms.

【 授权许可】

Unknown