A Robust Multilabel Feature Selection Approach Based on Graph Structure Considering Fuzzy Dependency and Feature Interaction

IEEE Transactions on Fuzzy Systems, Journal Year: 2023, Volume and Issue: 31(12), P. 4516 - 4528

Published: June 23, 2023

The performance of multilabel learning depends heavily on the quality input features. A mass irrelevant and redundant features may seriously affect learning, feature selection is an effective technique to solve this problem. However, most methods mainly emphasize removing these useless features, exploration interaction ignored. Moreover, widespread existence real-world data with uncertainty, ambiguity, noise limits selection. To end, our work dedicated designing efficient robust scheme. First, distribution character analyzed generate fuzzy multineighborhood granules. By exploring classification information implied in under granularity structure, a $k$ -nearest neighbor rough set model constructed, concept dependency studied. Second, series uncertainty measures approximation spaces are studied analyze correlations pairs, including interactivity. Third, by investigating measure between label, modeled as complete weighted graph. Then, vertices assessed iteratively guide assignment weights. Finally, graph structure-based algorithm (GRMFS) designed. experiments conducted 15 datasets. results verify superior GRMFS compared nine representative methods.

Language: Английский

---

Feature Selection Based on Weighted Fuzzy Rough Sets

IEEE Transactions on Fuzzy Systems, Journal Year: 2024, Volume and Issue: 32(7), P. 4027 - 4037

Published: April 16, 2024

Fuzzy rough set approaches have received widespread attention across the disciplines of feature selection and rule extraction. When calculating fuzzy degree membership a sample within specific class, traditional sets give precedence to distance information between other samples that do not belong often neglecting influence remoteness from specified class. In fact, this calculation strategy limits discriminability different relative given which may affect accuracy efficiency subset selection. To address shortcoming, present study puts forward new approach, weighted set, can more accurately measure correlation difference decision Based on model first defines importance class uses it as weight thereby constructing effective approximation operator. On basis, dependency variables conditional attributes is defined evaluate candidate features. Then, concept discrimination proposed, rationality discussed. Finally, based operator, algorithm for selecting features formulated. Experimental outcomes demonstrate performs well in terms performance, only smaller number features, but also achieving higher classification simplified data, showing its practical application value

Language: Английский

---

MFGAD: Multi-fuzzy granules anomaly detection

Information Fusion, Journal Year: 2023, Volume and Issue: 95, P. 17 - 25

Published: Feb. 8, 2023

Language: Английский

---

Outlier Detection Using Three-Way Neighborhood Characteristic Regions and Corresponding Fusion Measurement

IEEE Transactions on Knowledge and Data Engineering, Journal Year: 2023, Volume and Issue: 36(5), P. 2082 - 2095

Published: Sept. 5, 2023

Outliers carry significant information to reflect an anomaly mechanism, so outlier detection facilitates relevant data mining. In terms of detection, the classical approaches from distances apply numerical rather than nominal data, while recent methods on basic rough sets deal with data. Aiming at wide numerical, nominal, and hybrid this paper investigates three-way neighborhood characteristic regions corresponding fusion measurement advance detection. First, are deepened via decision, they derive structures model boundaries, inner regions, regions. Second, motivate weight regarding all features, thus, a multiple factor emerges establish new method detection; furthermore, algorithm (called 3WNCROD) is designed comprehensively process mixed Finally, 3WNCROD experimentally validated, it generally outperforms 13 contrast algorithms perform better for

Language: Английский

---

MAFCD: Multi-level and adaptive conditional diffusion model for anomaly detection

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 102965 - 102965

Published: Jan. 1, 2025

Language: Английский

---

Consistency-guided semi-supervised outlier detection in heterogeneous data using fuzzy rough sets

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 165, P. 112070 - 112070

Published: Aug. 8, 2024

Language: Английский

---

A novel outlier detection approach based on formal concept analysis

Knowledge-Based Systems, Journal Year: 2023, Volume and Issue: 268, P. 110486 - 110486

Published: March 20, 2023

Language: Английский

---

Boundary-aware local Density-based outlier detection

Information Sciences, Journal Year: 2023, Volume and Issue: 647, P. 119520 - 119520

Published: Aug. 12, 2023

Language: Английский

---

Fuzzy granular anomaly detection using Markov random walk

Information Sciences, Journal Year: 2023, Volume and Issue: 646, P. 119400 - 119400

Published: July 20, 2023

Language: Английский

---

Energy supplier selection by TOPSIS method based on multi-attribute decision-making by using novel idea of complex fuzzy rough information

Energy Strategy Reviews, Journal Year: 2024, Volume and Issue: 54, P. 101442 - 101442

Published: June 5, 2024

Language: Английский

---