Outlier detection method based on high-density iteration

Zhou, Yu; Xia, Hao; Yu, Dahui; Cheng, Jiaoyang; Li, Jichun

doi:10.1016/j.ins.2024.120286

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…Zhou et al [25] proposed a algorithm based on high-density iteration and extended k-neighbors to detect outliers. RDF [26] measures density by counting the number of neighbors within the neighborhood radius.…”

Section: Related Workmentioning

confidence: 99%

An Outlier Detection Algorithm Based on Local Density Feedback Outlier Factor

Zhang,

Hou,

Jia

et al. 2024

Preprint

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Neighborhood similarity assumes that adjacent objects should exhibit similar characteristics. To address the issue that previous density-based algorithms neglected neighborhood similarity and global information, resulting in low accuracy for outlier detection, we propose an outlier detection algorithm based on local density feedback outlier factor. Firstly, principal component analysis method is employed combining with natural neighbor search algorithm for density estimation. Subsequently, a density feedback mechanism is introduced for post-processing density values, which defines the density differences within the neighborhood as feedback signals, and update density feedback values for each object based on the principle of neighborhood similarity. Through multiple iterations of feedback, individual objects can aggregate global information. Finally, feedback outlier factor is defined to quantify outlier degrees. Experimental validation demonstrates the effectiveness of the proposed algorithm in enhancing outlier detection performance.

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Section: Related Workmentioning

confidence: 99%

An Outlier Detection Algorithm Based on Local Density Feedback Outlier Factor

Zhang,

Hou,

Jia

et al. 2024

Preprint

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Delineation of geochemical anomalies through empirical cumulative distribution function for mineral exploration

Shahrestani,

Sanislav

2025

Journal of Geochemical Exploration

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Automatic Filtering of Sugarcane Yield Data

da Silva,

Molin,

Wei

et al. 2024

AgriEngineering

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Sugarcane mechanized harvesting generates large volumes of data that are used to monitor harvesters’ functionalities. The dynamic interaction of the machine-onboard instrumentation–crop system introduces discrepant and noisy values into the data, requiring outlier detectors to support this complex and empirical decision. This study proposes an automatic filtering technique for sugarcane harvesting data to automate the process. A three-step automated filtering algorithm based on a sliding window was developed and further evaluated with four configurations of the maximum variation factor f and six SW sizes. The performance of the proposed method was assessed by using artificial outliers in the datasets with an outlier magnitude (OM) of ±0.01 to ±1.00. Three case studies with real crop data were presented to demonstrate the effectiveness of the proposed filter in detecting outliers of different magnitudes, compared to filtering by another method in the literature. In each dataset, the proposed filter detected nearly 100% of larger (OM = ±1.00 and ±0.80) and medium (OM = ±0.50) magnitudes’ outliers, and approximately 26% of smaller outliers (OM = ±0.10, ±0.05, and ±0.01). The proposed algorithm preserved wider ranges of data compared to the comparative method and presented equivalent results in the identification of regions with different productive potentials of sugarcane in the field. Therefore, the proposed method retained data that reflect sugarcane yield variability at the row level and it can be used in practical application scenarios to deal with large datasets obtained from sugarcane harvesters.

show abstract

Outlier detection method based on high-density iteration

Cited by 3 publications

References 33 publications

An Outlier Detection Algorithm Based on Local Density Feedback Outlier Factor

An Outlier Detection Algorithm Based on Local Density Feedback Outlier Factor

Delineation of geochemical anomalies through empirical cumulative distribution function for mineral exploration

Automatic Filtering of Sugarcane Yield Data

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