M-FCCL: Memory-based concept-cognitive learning for dynamic fuzzy data classification and knowledge fusion

Information Fusion, Journal Year: 2023, Volume and Issue: 100, P. 101962 - 101962

Published: Aug. 3, 2023

Language: Английский

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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

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Feature selection for classification with Spearman’s rank correlation coefficient-based self-information in divergence-based fuzzy rough sets

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 249, P. 123633 - 123633

Published: March 11, 2024

Language: Английский

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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

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Semi-supervised multi-sensor information fusion tailored graph embedded low-rank tensor learning machine under extremely low labeled rate

Information Fusion, Journal Year: 2023, Volume and Issue: 105, P. 102222 - 102222

Published: Dec. 30, 2023

Language: Английский

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Incremental Cognitive Learning Approach Based on Concept Reduction

International Journal of Approximate Reasoning, Journal Year: 2025, Volume and Issue: unknown, P. 109359 - 109359

Published: Jan. 1, 2025

Language: Английский

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Online boxplot derived outlier detection

International Journal of Data Science and Analytics, Journal Year: 2024, Volume and Issue: unknown

Published: May 20, 2024

Abstract Outlier detection is a widely used technique for identifying anomalous or exceptional events across various contexts. It has proven to be valuable in applications like fault detection, fraud and real-time monitoring systems. Detecting outliers real time crucial several industries, such as financial quality control manufacturing processes. In the context of big data, amount data generated enormous, traditional batch mode methods are not practical since entire dataset available. The limited computational resources further compound this issue. Boxplot algorithm outlier that involves derivations. However, lack an incremental closed form statistical calculations during boxplot construction poses considerable challenges its application within realm data. We propose incremental/online version address these challenges. Our proposed based on approximation approach numerical integration histogram calculation cumulative distribution function. This independent dataset’s distribution, making it effective all types distributions, whether skewed not. To assess efficacy algorithm, we conducted tests using simulated datasets featuring varying degrees skewness. Additionally, applied real-world concerning software which posed challenge. experimental results underscored robust performance our highlighting comparable access dataset. online method, leveraging define whiskers, consistently achieved results. Notably, demonstrated efficiency, maintaining constant memory usage with minimal hyperparameter tuning.

Language: Английский

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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

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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

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Unmanned Aerial Vehicles anomaly detection model based on sensor information fusion and hybrid multimodal neural network

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 132, P. 107961 - 107961

Published: Feb. 6, 2024

Language: Английский

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