Anomaly Detection on Shuttle data using Unsupervised Learning Techniques

Shriram, S.; Sivasankar, E.

doi:10.1109/iccike47802.2019.9004325

Cited by 25 publications

(6 citation statements)

References 9 publications

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“…• The performance on Cardio was one of the highlights at the time the LODES was proposed [41], with an AUC-ROC value of 0.7208 (MS2OD: 0.9271). • The highest AUC-ROC obtained via OneClassSVM in [49] is 0.99, similar to MS2OD (0.9924). This paper was devoted to various unsupervised ML models only on Shuttle.…”

Section: Comparative Analysis With Dataset-targeting and Ensemble Lea...mentioning

confidence: 75%

MS2OD: outlier detection using minimum spanning tree and medoid selection

Li,

Wang

et al. 2024

Mach. Learn.: Sci. Technol.

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As an essential task in data mining, outlier detection identifies abnormal patterns in numerous applications, among which clustering-based outlier detection is one of the most popular methods for its effectiveness in detecting cluster-related outliers, especially in medical applications. This article presents an advanced method to extract cluster-based outliers by employing a scaled minimum spanning tree (MST) data structure and a new medoid selection method: 1. We compute a scaled MST and iteratively cut the current longest edge to obtain clusters; 2. We apply a new medoid selection method, considering the noise effect to improve the quality of cluster-based outlier identification. The experimental results on real-world data, including extensive medical corpora and other semantically meaningful datasets, demonstrate the wide applicability and outperforming metrics of the proposed method.

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Section: Comparative Analysis With Dataset-targeting and Ensemble Lea...mentioning

confidence: 75%

MS2OD: outlier detection using minimum spanning tree and medoid selection

Li,

Wang

et al. 2024

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…To assess performance, we used the performance indicators stated in Equations ( 1)-( 6) to compare our proposed system to other systems. The proposed evaluation parameters were taken from existing research work [51,52].…”

Section: Assessment Methodsmentioning

confidence: 99%

Unsupervised Outlier Detection in IOT Using Deep VAE

Gouda

Tahir

Alanazi

et al. 2022

Sensors

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The Internet of Things (IoT) refers to a system of interconnected, internet-connected devices and sensors that allows the collection and dissemination of data. The data provided by these sensors may include outliers or exhibit anomalous behavior as a result of attack activities or device failure, for example. However, the majority of existing outlier detection algorithms rely on labeled data, which is frequently hard to obtain in the IoT domain. More crucially, the IoT’s data volume is continually increasing, necessitating the requirement for predicting and identifying the classes of future data. In this study, we propose an unsupervised technique based on a deep Variational Auto-Encoder (VAE) to detect outliers in IoT data by leveraging the characteristic of the reconstruction ability and the low-dimensional representation of the input data’s latent variables of the VAE. First, the input data are standardized. Then, we employ the VAE to find a reconstructed output representation from the low-dimensional representation of the latent variables of the input data. Finally, the reconstruction error between the original observation and the reconstructed one is used as an outlier score. Our model was trained only using normal data with no labels in an unsupervised manner and evaluated using Statlog (Landsat Satellite) dataset. The unsupervised model achieved promising and comparable results with the state-of-the-art outlier detection schemes with a precision of ≈90% and an F1 score of 79%.

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“…They took into account RSS values for indoor localisation in IoT sensor networks and also identified outliers. Wang et al [14] employed an adaptive neural control system in conjunction with an active power filter and a fuzzy sliding controller. Jap and Bhasin [15] used a variety of techniques that included supervised and unsupervised approaches to uncover outliers and identify the hardware Trojan issue.…”

Section: Related Workmentioning

confidence: 99%

A hybrid approach for efficient outlier detection using supervised and unsupervised techniques

Jayaramulu,

Venkateswarlu

2024

Preprint

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Due to data imbalance and dimensionality, it is difficult to achieve optimal performance when detecting outliers in high-dimensional data. Numerous algorithms were developed in try to solve this issue. However, they have their advantages in identifying outliers from such data and are created using either supervised learning technique or unsupervised learning. While unsupervised learning techniques offer mechanisms for discovering and utilising complicated patterns, supervised learning techniques make use of training data. This paper's key premise is that you may "combine two methodologies to create a hybrid and reap the benefits of both worlds." We put forth a cutting-edge machine learning (ML) framework to evaluate this claim, combining supervised and unsupervised techniques for effective outlier detection. Additionally, we suggested an approach called the Multi-Model Approach for Outlier Detection (MMA-OD). The technique improves performance by utilising the advantages of both supervised and unsupervised learning models. Its strength is getting a better feature space. With several benchmark datasets, the suggested approach is assessed. According to the empirical findings, MMA-OD performs better than many other techniques.

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Anomaly Detection on Shuttle data using Unsupervised Learning Techniques

Cited by 25 publications

References 9 publications

MS2OD: outlier detection using minimum spanning tree and medoid selection

MS2OD: outlier detection using minimum spanning tree and medoid selection

Unsupervised Outlier Detection in IOT Using Deep VAE

A hybrid approach for efficient outlier detection using supervised and unsupervised techniques

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