PlumX Metrics
Embed PlumX Metrics

Feature selection for label distribution learning using Dempster-Shafer evidence theory

Applied Intelligence, ISSN: 1573-7497, Vol: 55, Issue: 4
2025
  • 0
    Citations
  • 0
    Usage
  • 0
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Article Description

In the contemporary epoch of massive data, the fuzziness of labels and the high dimensionality of feature space are prevalent characteristics of data. As a mathematical methodology for managing uncertainty, Dempster-Shafer evidence theory has found widespread applications in artificial intelligence, pattern recognition, and decision analysis. However, it has not garnered adequate attention in label distribution learning (LDL). This paper studies feature selection for LDL using Dempster-Shafer evidence theory. First, for a LDL data, distance maps in the feature space and in the label space are given, respectively. Furthermore, a tunable parameter to regulate the proximity level of features or labels is implemented. Then, the α-upper and α-lower approximations in the LDL data are put forward. Subsequently, to alleviate the influence of uncertainty on classification performance, robust feature evaluation measures for a LDL data, namely, “belief map" and “plausibility map" are defined, and they are based on the approximations. Next, feature selection algorithms utilizing belief and plausibility maps are specially designed. Finally, experimental results and statistical analyses demonstrate that the defined belief and plausibility maps can effectively measure the indeterminacy of LDL data, and the designed feature selection algorithms outperform five existing algorithms regarding classification performance.

Provide Feedback

Have ideas for a new metric? Would you like to see something else here?Let us know