A Comprehensive Survey of Anomaly Detection Algorithms

Samariya, Durgesh; Thakkar, Amit

doi:10.1007/s40745-021-00362-9

Cited by 54 publications

(15 citation statements)

References 79 publications

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“…Some of these were adopted by previous research studies such as, isolation tree/forest (Liu et al , 20028), nearest neighbor (Radovanović et al , 2014), clustering (li et al , 2022), statistical methods (Zimek and Filzmoser, 2018). In this study, more emphasis is given on unsupervised algorithms, although supervised learning may also be applicable because learning the expected behavior is considerably easier than learning the types of anomalies (Samariya and Thakkar, 2021).…”

Section: Discussionmentioning

confidence: 99%

Anomaly detection in the course evaluation process: a learning analytics–based approach

Vaidya

Sharma

2023

ITSE

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Purpose Course evaluations are formative and are used to evaluate learnings of the students for a course. Anomalies in the evaluation process can lead to a faulty educational outcome. Learning analytics and educational data mining provide a set of techniques that can be conveniently applied to extensive data collected as part of the evaluation process to ensure remedial actions. This study aims to conduct an experimental research to detect anomalies in the evaluation methods. Design/methodology/approach Experimental research is conducted with scientific approach and design. The researchers categorized anomaly into three categories, namely, an anomaly in criteria assessment, subject anomaly and anomaly in subject marks allocation. The different anomaly detection algorithms are used to educate data through the software R, and the results are summarized in the tables. Findings The data points occurring in all algorithms are finally detected as an anomaly. The anomaly identifies the data points that deviate from the data set’s normal behavior. The subject which is consistently identified as anomalous by the different techniques is marked as an anomaly in evaluation. After identification, one can drill down to more details into the title of anomalies in the evaluation criteria. Originality/value This paper proposes an analytical model for the course evaluation process and demonstrates the use of actionable analytics to detect anomalies in the evaluation process.

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Section: Discussionmentioning

confidence: 99%

Anomaly detection in the course evaluation process: a learning analytics–based approach

Vaidya

Sharma

2023

ITSE

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“…This work aims to develop a framework for detecting speech motion anomalies in conjunction with their corresponding speech acoustics. Anomaly detection 1,2 is a fundamental problem in data mining. It is noted that a substantial portion of prior work in the domain of anomaly detection has primarily directed its attention towards datasets originating from a singular data source.…”

Section: Introductionmentioning

confidence: 99%

Speech motion anomaly detection via cross-modal translation of 4D motion fields from tagged MRI

Liu,

Xing,

Zhuo

et al. 2024

Medical Imaging 2024: Image Processing

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Understanding the relationship between tongue motion patterns during speech and their resulting speech acoustic outcomes-i.e., articulatory-acoustic relation-is of great importance in assessing speech quality and developing innovative treatment and rehabilitative strategies. This is especially important when evaluating and detecting abnormal articulatory features in patients with speech-related disorders. In this work, we aim to develop a framework for detecting speech motion anomalies in conjunction with their corresponding speech acoustics. This is achieved through the use of a deep cross-modal translator trained on data from healthy individuals only, which bridges the gap between 4D motion fields obtained from tagged MRI and 2D spectrograms derived from speech acoustic data. The trained translator is used as an anomaly detector, by measuring the spectrogram reconstruction quality on healthy individuals or patients. In particular, the cross-modal translator is likely to yield limited generalization capabilities on patient data, which includes unseen out-of-distribution patterns and demonstrates subpar performance, when compared with healthy individuals. A one-class SVM is then used to distinguish the spectrograms of healthy individuals from those of patients. To validate our framework, we collected a total of 39 paired tagged MRI and speech waveforms, consisting of data from 36 healthy individuals and 3 tongue cancer patients. We used both 3D convolutional and transformer-based deep translation models, training them on the healthy training set and then applying them to both the healthy and patient testing sets. Our framework demonstrates a capability to detect abnormal patient data, thereby illustrating its potential in enhancing the understanding of the articulatory-acoustic relation for both healthy individuals and patients.

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“…The detection of outliers in real data has attracted great attention nowadays, it has been the important issue in statistical machine learning due to the requirement of robust. It has been also widely applied in various fields, for example, in the judgment of credit card fraud and fraudulent behavior in insurance claims, the medical diagnosis, see Boukerche et al (2020), Smiti (2020), Hassan (2022), Samariya and Thakkar (2023) and references therein. In practise, some values may deviate significantly from most data values, but from a statistical point of view, it seems different from white noise.…”

Section: Introductionmentioning

confidence: 99%

Outlier detection in classification based on feature-selection-based regression

Su,

Liu,

Cui

2024

Preprint

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The appearance of outliers results in a complexity to achieve an accurate classify. This paper aims to the detection and identification of outlier before selecting a suitable classifier. The problem is firstly converted to an high-dimensional regression, then we propose a novel method on combination of multiple-correlation-coefficientbased feature selection for dimensional reduction, t−test for sparsification, an iterated algorithm is also given. Performance on simulated numerical data and applications to low-dimensional iris data as well as high-dimensional DBWORD E-mail data demonstrate the superiority of the proposed method in outlier identification.

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A Comprehensive Survey of Anomaly Detection Algorithms

Cited by 54 publications

References 79 publications

Anomaly detection in the course evaluation process: a learning analytics–based approach

Anomaly detection in the course evaluation process: a learning analytics–based approach

Speech motion anomaly detection via cross-modal translation of 4D motion fields from tagged MRI

Outlier detection in classification based on feature-selection-based regression

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