Improving outliers detection in data streams using LiCS and voting

Benjelloun, Fatima-Zahra; Oussous, Ahmed; Bennani, Amine; Belfkih, Samir; Lahcen, Ayoub Ait

doi:10.1016/j.jksuci.2019.08.003

Cited by 9 publications

(10 citation statements)

References 14 publications

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“…The proposed framework is evaluated by comparing results with its standalone base learners and other some predictive machine learning techniques such as Logistic Regression (LR), SVM, GaussianNB, K-NN, Artificial Neural Network (ANN), Gradient Boosting, and Local Outlier Factor (LOF). In addition, ESOD is compared with many state-ofthe-art methods found in [14], [15], [21], [22], [26], [27], and [28]. We individually show the performance of the comparisons on the aforementioned 11 datasets using the accuracy, precision, recall, and F1-score metrics.…”

Section: E Results and Discussionmentioning

confidence: 99%

“…Regarding the density-based methods, there are some state-of-the-art methods developed to detect outliers in data streams, such as [18][19][20]. Another notable attempt was presented in [21], a method called LiCS was introduced to detect outlier that classifies the samples using Knearest neighbors of each node. Most recently, [22] proposed an incremental local density and cluster-based outlier factor method for detecting outliers in streaming data called iLDCBOF.…”

Section: Related Workmentioning

confidence: 99%

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An Effective Ensemble-based Framework for Outlier Detection in Evolving Data Streams

Hassan¹,

Barakat²,

Rezk³

2022

IJACSA

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In the last few years, data streams have drawn lots of researchers' attention due to their various applications, such as healthcare monitoring systems, fraud and intrusion detection, the internet of things (IoT), and financial market applications. A data stream is an unbounded sequence of data continually generated over time and is prone to evolution. Outliers in streaming data are the elements that significantly deviate from the majority of elements and then have to be detected as they may be error values or events of interest. Detection of outliers is a challenging issue in streaming data and is one of the most crucial tasks in data stream mining. Existing outlier detection methods for static data are unsuitable for use in data stream settings due to the unique characteristics of streaming data such as unpredictability, uncertainty, high-dimensionality, and changes in data distribution. Thus, in this paper, a novel ensemble learning framework called Ensemble-based Streaming Outlier Detection (ESOD) is presented to perfectly detect outliers over streaming data using a sliding window technique that is updated in response to the incoming events from the data streaming environment to overcome the concept evolution nature of streaming data. The proposed framework has three phases, namely the training phase, testing/offline phase, and outlier detection/online phase. A detection weighted vote technique is used to determine the final decisions for potential outliers. In the extensive experimental study, which was conducted on 11 realworld benchmark datasets, the proposed framework was assessed using many accuracy metrics. The experiment results showed that the proposed framework beats many other state-ofthe-art methods.

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Section: E Results and Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Effective Ensemble-based Framework for Outlier Detection in Evolving Data Streams

Hassan¹,

Barakat²,

Rezk³

2022

IJACSA

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“…Benjelloun et al [11] improved the ability to identify outliers of distance-based algorithm and microcluster-based algorithm (MCOD). This is by adding a layer named LiCS that categorizes k-nearest neighbor (KNN) of nodes according to the evolutionary conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Outlier Detection with Feature Selection Enabled Streaming Data Classification

Rajakumar¹,

Devi²

2023

Intelligent Automation &Amp; Soft Computing

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“…The extreme value identifications were carried out via standardization at the value of two and three standard deviations from the mean. Various predictive methods have been used for detection of outliers in the dataset [23][24][25][26][27]. The authors remarked that neural networks and linear models are sensitive to noise points, whereas decision trees are robust to outliers.…”

Section: Related Work and Problem Statementmentioning

confidence: 99%

Balancing Complex Signals for Robust Predictive Modeling

Aman

Rauf

Ali

et al. 2021

Sensors

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Robust predictive modeling is the process of creating, validating, and testing models to obtain better prediction outcomes. Datasets usually contain outliers whose trend deviates from the most data points. Conventionally, outliers are removed from the training dataset during preprocessing before building predictive models. Such models, however, may have poor predictive performance on the unseen testing data involving outliers. In modern machine learning, outliers are regarded as complex signals because of their significant role and are not suggested for removal from the training dataset. Models trained in modern regimes are interpolated (over trained) by increasing their complexity to treat outliers locally. However, such models become inefficient as they require more training due to the inclusion of outliers, and this also compromises the models’ accuracy. This work proposes a novel complex signal balancing technique that may be used during preprocessing to incorporate the maximum number of complex signals (outliers) in the training dataset. The proposed approach determines the optimal value for maximum possible inclusion of complex signals for training with the highest performance of the model in terms of accuracy, time, and complexity. The experimental results show that models trained after preprocessing with the proposed technique achieve higher predictive accuracy with improved execution time and low complexity as compared to traditional predictive modeling.

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Improving outliers detection in data streams using LiCS and voting

Cited by 9 publications

References 14 publications

An Effective Ensemble-based Framework for Outlier Detection in Evolving Data Streams

An Effective Ensemble-based Framework for Outlier Detection in Evolving Data Streams

A Novel Outlier Detection with Feature Selection Enabled Streaming Data Classification

Balancing Complex Signals for Robust Predictive Modeling

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