Feature Selection With Missing Labels Using Multilabel Fuzzy Neighborhood Rough Sets and Maximum Relevance Minimum Redundancy
| Autor: | Jiucheng Xu, Lin Sun, Tengyu Yin, Yuhua Qian, Weiping Ding |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Computer science
business.industry Applied Mathematics Feature selection Pattern recognition 02 engineering and technology Mutual information Fuzzy logic Redundancy (information theory) Computational Theory and Mathematics Artificial Intelligence Control and Systems Engineering Margin (machine learning) Robustness (computer science) 0202 electrical engineering electronic engineering information engineering Entropy (information theory) 020201 artificial intelligence & image processing Rough set Artificial intelligence business |
| Zdroj: | IEEE Transactions on Fuzzy Systems. 30:1197-1211 |
| ISSN: | 1941-0034 1063-6706 |
| DOI: | 10.1109/tfuzz.2021.3053844 |
| Popis: | Recently, multilabel classification has generated considerable research interest. However, the high dimensionality of multilabel data incurs high costs; moreover, in many real applications, a number of labels of training samples are randomly missed. Thus, multilabel classification can have great complexity and ambiguity, which means some feature selection methods exhibit poor robustness and yield low prediction accuracy. To solve these issues, this paper presents a novel feature selection method based on multilabel fuzzy neighborhood rough sets (MFNRS) and maximum relevance minimum redundancy (MRMR) that can be used on multilabel data with missing labels. First, to handle multilabel data with missing labels, a relation coefficient of samples, label complement matrix, and label-specific feature matrix are constructed and implemented in a linear regression model to recover missing labels. Second, the margin-based fuzzy neighborhood radius, fuzzy neighborhood similarity relationship, and fuzzy neighborhood information granule are developed. The MFNRS model is built based on multilabel neighborhood rough sets combined with fuzzy neighborhood rough sets. Based on algebra and information views, certain fuzzy neighborhood entropy-based uncertainty measures are proposed for MFNRS. The fuzzy neighborhood mutual information-based MRMR model with label correlation is improved to evaluate the performance of candidate features. Finally, a feature selection algorithm is designed to improve the performance for multilabel data with missing labels. Experiments on twenty datasets verify that our method is effective not only for recovering missing labels but also for selecting significant features with better classification performance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|