Local rough set-based feature selection for label distribution learning with incomplete labels

Qian, Wei; Dong, Ping; Wang, Yinglong; Dai, Shuguang; Huang, Jintao

doi:10.1007/s13042-022-01528-4

Cited by 9 publications

(1 citation statement)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate our proposed label distribution feature selection algorithm, we employed seven different algorithms, including four MLL algorithms(GRRO [40], BRReliefF [41], MReliefF [42]and MDDMspc [43]) and three LDL algorithms(LDFS [20], LRFS [44], and FSFL [33]). The first four feature selection methods are designed for multi-label data, so it is necessary to discretize the label distribution data before conducting experiments.…”

Section: Comparisions Of Algorithm Mclfs With Other Algorithms 531 Ex...mentioning

confidence: 99%

Weakly-supervised label distribution feature selection via label-specific features and label correlation

Shu,

Hu,

Qian

2024

Preprint

Self Cite

View full text Add to dashboard Cite

Label Distribution Learning (LDL) stands out as an innovative method to address the challenges posed by label ambiguity. Current LDL algorithms are predominantly designed for datasets with comprehensive supervised information. However, in real-world scenarios, it’s common to encounter partial missing labels within the label space. This phenomenon disrupts the structure and correlation between labels, posing a challenge to the precise design of learning algorithms. Furthermore, in label distribution learning, it also faces the effect of high feature dimensionality. To tackle this, adopting pre-processing methods like feature selection becomes crucial, aiming to trim down the data dimensionality. Motivated by this, a weakly-supervised label distribution feature selection algorithm based on label correlation is proposed in this paper. First, to handle weakly-supervised label distribution data, a two-stage incomplete label distribution learning method (IncomLDLTS) to recover missing labels by exploiting label-independent prediction based on label-specific features is proposed. Second, a minimum correlation label feature selection algorithm (MCLFS) to enhance the performance for complete label distribution data is designed, which employs the interaction information metric to explore the label correlation to obtain a minimum correlation label, then features about each label based on label-specific features and feature redundancy are captured. Finally, based on six representative evaluation metrics, our experiments across 14 datasets affirm the effectiveness of our approach, not only in restoring missing labels but also in choosing essential features, leading to enhanced classification accuracy.

show abstract

Section: Comparisions Of Algorithm Mclfs With Other Algorithms 531 Ex...mentioning

confidence: 99%

Weakly-supervised label distribution feature selection via label-specific features and label correlation

Shu,

Hu,

Qian

2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

TSFNFS: two-stage-fuzzy-neighborhood feature selection with binary whale optimization algorithm

Sun

Wang

Ding

et al. 2022

Int. J. Mach. Learn. & Cyber.

View full text Add to dashboard Cite

Multi-label feature selection via joint label enhancement and pairwise label correlations

Liu

Yang

Lin

et al. 2023

Int. J. Mach. Learn. & Cyber.

View full text Add to dashboard Cite

Local rough set-based feature selection for label distribution learning with incomplete labels

Cited by 9 publications

References 56 publications

Weakly-supervised label distribution feature selection via label-specific features and label correlation

Weakly-supervised label distribution feature selection via label-specific features and label correlation

TSFNFS: two-stage-fuzzy-neighborhood feature selection with binary whale optimization algorithm

Multi-label feature selection via joint label enhancement and pairwise label correlations

Contact Info

Product

Resources

About