Identifying Outliers via Local Granular-Ball Density
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces local Granular-Ball Density-based Outlier (GBDO) detection, a novel method improving outlier detection by processing data at multiple granularities. GBDO enhances robustness and reduces computational complexity compared to traditional methods.
Area Of Science
- Data Mining
- Machine Learning
- Computational Intelligence
Background
- Existing density-based outlier detection methods operate at a single data granularity, leading to high sensitivity to noise and failure to capture multi-level information.
- These methods often overlook data uncertainty, such as fuzziness, hindering effective outlier detection.
Purpose Of The Study
- To propose a novel outlier detection method, local Granular-Ball Density-based Outlier (GBDO), leveraging Granular-Ball Computing (GBC).
- To enhance the performance of density-based outlier detection by addressing limitations of single-granularity processing and uncertainty handling.
Main Methods
- Utilizing Granular-Ball Computing (GBC) for multi-granularity data processing.
- Identifying k-similarity Granular-Ball (GB) neighborhoods based on fuzzy relations.
- Calculating local reachability similarity density for GBs.
- Computing local GB outlier factors for samples.
Main Results
- GBDO adopts a multi-granularity processing paradigm using GBs as fundamental units.
- This approach reduces computational complexity and increases robustness against noisy data.
- Experimental results validate the effectiveness of GBDO against state-of-the-art methods.
Conclusions
- GBDO offers an effective solution for outlier detection by integrating multi-granularity processing and robustness.
- The method successfully overcomes the limitations of traditional single-granularity density-based approaches.
- The proposed GBDO method demonstrates superior performance in identifying outliers.
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