.png){kind=icon}
.png){kind=icon}
Return
Efficient Attribute Reduction Algorithms Using Discernibility Attributes for Hierarchical Classification
.png){kind=icon}
PRE
AI
DOI：10.3390/sym18040609.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Attribute reduction, also referred to as feature selection, focuses on seeking a minimal attribute subset and is an important topic in rough set theory (RST). Classical rough set-based attribute reduction is restricted to decision systems with a single label level and fails to obtain attribute reducts across hierarchical label levels, leading to low computational efficiency. To tackle this issue, this paper proposes a novel method to derive bidirectional reducts across label levels. We first establish the reduction relationship from the coarse label level to the fine label level by analyzing the relationships among decision classes at different label levels. Correspondingly, we construct the reduction relationship from the fine label level to the coarse label level by investigating the connections between positive regions across different label levels. Based on these relationships, two efficient attribute reduction algorithms are developed, which can rapidly compute a reduct at one label level from the reduct at another level. Experimental results on twelve UCI datasets demonstrate that the proposed algorithms require less running time than the other four methods while still achieving high classification accuracy.
Keywords:
rough sets
attribute reduction
granular computing
feature selection
uncertainty analysis