ADD: a new average divergence difference-based outlier detection method with skewed distribution of data objects

Xiong, Zenggang; Gao, Qin-Qin; Gao, Qiang; Zhang, Yufang; Li, Lin-Tao; Zhang, Min

doi:10.1007/s10489-021-02399-y

Cited by 7 publications

(2 citation statements)

References 27 publications

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“…First, the IForest does not depend on distance, and time cost does not relate to data dimension, which is a linear time complexity, and second, the IForest can deal with large datasets and is an ensemble method. The more there are ITrees, the more stable is the IForest [19]. Although IForest is suitable for anomaly detection of high-dimensional datasets, the detection efficiency will decrease with data distribution complexity.…”

Section: Iforestmentioning

confidence: 99%

The Optimized Anomaly Detection Models Based on an Approach of Dealing with Imbalanced Dataset for Credit Card Fraud Detection

Zhang

Lü

Lin

et al. 2022

Mobile Information Systems

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Credit card fraud is a major problem in today’s financial world. It induces severe damage to financial institutions and individuals. There has been an exponential increase in the losses due to fraud in recent years. Hence, effectively detecting fraudulent behavior is of vital importance for either financial institutions or individuals. Since credit fraud events account for a small proportion of all transaction events in real life, the datasets about credit fraud are usually imbalanced. Some common classifiers, such as decision tree and naïve Bayes, are unable to detect fraud. Furthermore, in some cases, traditional strategies for dealing with an imbalanced problem, such as the synthetic minority oversampling technique (SMOTE), are not effective for the fraud detection datasets. To accurately detect fraud behavior, this study uses anomaly detection on imbalanced data, as well as Isolation Forest (IForest) with kernel principal component analysis. A one-class support vector machine (OCSVM) with AdaBoost is used as two models to detect outliers which significantly improves detection accuracy and efficiency. The model achieved 96% accuracy, 100% precision, 96% recall, and 98% F 1 score, respectively. The proposed model is expected to become a helpful tool for finding credit card fraud detection, and the analysis presented in this study will provides useful insights into credit card fraud detection mechanisms.

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Section: Iforestmentioning

confidence: 99%

The Optimized Anomaly Detection Models Based on an Approach of Dealing with Imbalanced Dataset for Credit Card Fraud Detection

Zhang

Lü

Lin

et al. 2022

Mobile Information Systems

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show abstract

“…Local-gravitation outlier detection (LGOD) [25] introduces the concept of inter-sample gravity, which determines the extent of the anomalies by calculating the change in the gravity of a sample's nearest neighbours. The average divergence difference (ADD) [26] algorithm introduces the concept of average divergence difference to improve the accuracy of local outlier detection. Although the density of clusters in the datasets used varies greatly, the proposed methods have certain effects.…”

Section: Introductionmentioning

confidence: 99%

Density-Distance Outlier Detection Algorithm Based on Natural Neighborhood

Zhang

Yang

2023

Axioms

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Outlier detection is of great significance in the domain of data mining. Its task is to find those target points that are not identical to most of the object generation mechanisms. The existing algorithms are mainly divided into density-based algorithms and distance-based algorithms. However, both approaches have some drawbacks. The former struggles to handle low-density modes, while the latter cannot detect local outliers. Moreover, the outlier detection algorithm is very sensitive to parameter settings. This paper proposes a new two-parameter outlier detection (TPOD) algorithm. The method proposed in this paper does not need to manually define the number of neighbors, and the introduction of relative distance can also solve the problem of low density and further accurately detect outliers. This is a combinatorial optimization problem. Firstly, the number of natural neighbors is iteratively calculated, and then the local density of the target object is calculated by adaptive kernel density estimation. Secondly, the relative distance of the target points is computed through natural neighbors. Finally, these two parameters are combined to obtain the outlier factor. This eliminates the influence of parameters that require users to determine the number of outliers themselves, namely, the top-n effect. Two synthetic datasets and 17 real datasets were used to test the effectiveness of this method; a comparison with another five algorithms is also provided. The AUC value and F1 score on multiple datasets are higher than other algorithms, indicating that outliers can be found accurately, which proves that the algorithm is effective.

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A double-weighted outlier detection algorithm considering the neighborhood orientation distribution of data objects

Gao

Xiong

et al. 2023

Appl Intell

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ADD: a new average divergence difference-based outlier detection method with skewed distribution of data objects

Cited by 7 publications

References 27 publications

The Optimized Anomaly Detection Models Based on an Approach of Dealing with Imbalanced Dataset for Credit Card Fraud Detection

The Optimized Anomaly Detection Models Based on an Approach of Dealing with Imbalanced Dataset for Credit Card Fraud Detection

Density-Distance Outlier Detection Algorithm Based on Natural Neighborhood

A double-weighted outlier detection algorithm considering the neighborhood orientation distribution of data objects

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