Comparative analysis of machine learning models for anomaly detection in manufacturing

Kharitonov, Andrey; Nahhas, Abdulrahman; Pohl, Matthias; Turowski, Klaus

doi:10.1016/j.procs.2022.01.330

Cited by 20 publications

(7 citation statements)

References 20 publications

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“…Adopting the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology in our investigation into the capabilities of an isolation forest ensemble for detecting stock market manipulation offers a structured, iterative, and comprehensive framework that significantly enhances the study's scientific rigor and practical applicability. The CRISP-DM methodology has been previously and successfully employed in other machine learning projects, as evidenced by the literature [21][22][23]. By meticulously following CRISP-DM's phases-from understanding the business problem and data to model evaluation and deployment-we ensure a deep alignment between our models and the real-world phenomenon of market manipulation.…”

Section: Methodsmentioning

confidence: 99%

Ensemble Approach Using k-Partitioned Isolation Forests for the Detection of Stock Market Manipulation

Núñez Delafuente,

Astudillo,

Díaz

2024

Mathematics

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Stock market manipulation, defined as any attempt to artificially influence stock prices, poses significant challenges by causing financial losses and eroding investor trust. The prevalent reliance on supervised learning models for detecting such manipulations, while showing promise, faces notable hurdles due to the dearth of labeled data and the inability to recognize novel manipulation tactics beyond those explicitly labeled. This study ventures into addressing these gaps by proposing a novel detection framework aimed at identifying suspicious hourly manipulation blocks through an unsupervised learning approach, thereby circumventing the limitations of data labeling and enhancing the adaptability to emerging manipulation strategies. Our methodology involves the innovative creation of features reflecting the behavior of stocks across various time windows followed by the segmentation of the dataset into k subsets. This setup facilitates the identification of potential manipulation instances via a voting ensemble composed of k isolation forest models, which have been chosen for their efficiency in pinpointing anomalies and their linear computational complexity—attributes that are critical for analyzing vast datasets. Evaluated against eight real stocks known to have undergone manipulation, our approach demonstrated a remarkable capability to identify up to 89% of manipulated blocks, thus significantly outperforming previous methods that do not utilize a voting ensemble. This finding not only surpasses the detection rates reported in prior studies but also underscores the enhanced robustness and adaptability of our unsupervised model in uncovering varied manipulation schemes. Through this research, we contribute to the field by offering a scalable and efficient unsupervised learning strategy for stock manipulation detection, thereby marking a substantial advancement over traditional supervised methods and paving the way for more resilient financial markets.

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Section: Methodsmentioning

confidence: 99%

Ensemble Approach Using k-Partitioned Isolation Forests for the Detection of Stock Market Manipulation

Núñez Delafuente,

Astudillo,

Díaz

2024

Mathematics

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“…In the nanocrystalline grain growth example provided in the previous section, the anomalous spikes in the diffraction pattern were identified automatically by an explicit search algorithm and a traditional statistical threshold approach based on the Z-score [14]. There are many different approaches for anomaly detection; relevant example references relating to anomaly detection in microstructure and materials manufacturing are provided here for several techniques: k-nearest neighbour [41], local outlier factor and isolation forests [42,43], support vector machines [44], replicator neural networks [45], autoencoders [46], long short-term memory neural networks [47], Bayesian networks [48], hidden Markov models [49,50], cluster-analysis methods [51,52], and feature bagging [53]. While many of these techniques are unsupervised and can automatically detect anomalies, some methods like decision trees and k-nearest neighbours typically require supervised training by an expert and therefore foreknowledge of the anomaly.…”

Section: The Role Of Computational Methodsmentioning

confidence: 99%

Microstructural Black Swans

Boyce

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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Black swans are a metaphor for rare events with extreme consequences. In the domain of structural materials, black swans represent features in the microstructure that lead to catastrophic failure; as a result of their rarity, they are difficult to observe and often overlooked. These unusual weakest-link features are described variously as incipient, emergent, or anomalous. They give rise to localization, percolation, or avalanche events such as fracture, ductile rupture, dielectric breakdown, corrosion pit nucleation, and fatigue-crack initiation; as such, they are limiting cases in the concept of a representative volume. In this perspective, three examples are given of rare microstructural features and how they limit the mechanical reliability of structural metals. After taking stock of these examples, a future outlook considers the need for high-throughput testing and non-destructive characterization as well as detection algorithms and materials modelling strategies, including accelerated machine learning methods, that can capture anomalous events.

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“…Consequently, the approach enabled successful detection of defective engines with high probability before shipment. Kharitonov et al [24] conducted an evaluation of ten machine learning models for the detection of anomalies in the manufacturing field, including unsupervised learning methods such as k-nearest neighbors (KNN), Autoencoder, LOF, and COPOD. These methods do not require prior knowledge of abnormal patterns but rely on understanding the characteristics and structure of the data.…”

Section: Cases Of Potential Failure Identificationmentioning

confidence: 99%

Preemptive Failure Detection using Contamination-Based Stacking Ensemble in Missiles

2024

KSII TIIS

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Comparative analysis of machine learning models for anomaly detection in manufacturing

Cited by 20 publications

References 20 publications

Ensemble Approach Using k-Partitioned Isolation Forests for the Detection of Stock Market Manipulation

Ensemble Approach Using k-Partitioned Isolation Forests for the Detection of Stock Market Manipulation

Microstructural Black Swans

Preemptive Failure Detection using Contamination-Based Stacking Ensemble in Missiles

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