Anomaly Detection Method Based on Fast Local Subspace Classifier

Shibuya, Hitoshi; Maeda, Shunji

doi:10.1002/ecj.11770

Cited by 6 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LSC determines outlier measure based on time increment for distance applied on the model. This method was improved in terms of computation in [15] by proposing method Fast LSC. In this approach, clustering is used to reduce the amount of data and hence proves ten times faster as compared to the LSC method.…”

Section: Related Workmentioning

confidence: 99%

Local Neighborhood-based Outlier Detection of High Dimensional Data using different Proximity Functions

Rehman¹,

Muhammad²

2020

IJACSA

View full text Add to dashboard Cite

In recent times, dimension size has posed more challenges as compared to data size. The serious concern of high dimensional data is the curse of dimensionality and has ultimately caught the attention of data miners. Anomaly detection based on local neighborhood like local outlier factor has been admitted as state of art approach but fails when operated on the high number of dimensions for the reason mentioned above. In this paper, we determine the effects of different distance functions on an unlabeled dataset while digging outliers through the density-based approach. Further, we also explore findings regarding runtime and outlier score when dimension size and number of nearest neighbor points (min_pts) are varied. This analytic research is also very appropriate and applicable in the domain of big data and data science as well.

show abstract

Section: Related Workmentioning

confidence: 99%

Local Neighborhood-based Outlier Detection of High Dimensional Data using different Proximity Functions

Rehman¹,

Muhammad²

2020

IJACSA

View full text Add to dashboard Cite

show abstract

“…Bin Yao and Hutchison (2014) proposed a densitybased local outlier detection method (LOF) for uncertain data. H. Shibuya and Maeda (2016) developed an anomaly detection method based on multidimensional time-series sensor data and using normal state models. Principal component analysis (Li and Wen, 2014) could be used for linear models; and the Gaussian mixture model (GMM) (Dai and Gao, 2013), isolation forest (F.T.…”

Section: Introductionmentioning

confidence: 99%

Abnormal ECG detection based on an adversarial autoencoder

et al. 2022

View full text Add to dashboard Cite

Automatic detection and alarm of abnormal electrocardiogram (ECG) events play an important role in an ECG monitor system; however, popular classification models based on supervised learning fail to detect abnormal ECG effectively. Thus, we propose an ECG anomaly detection framework (ECG-AAE) based on an adversarial autoencoder and temporal convolutional network (TCN) which consists of three modules (autoencoder, discriminator, and outlier detector). The ECG-AAE framework is trained only with normal ECG data. Normal ECG signals could be mapped into latent feature space and then reconstructed as the original ECG signal back in our model, while abnormal ECG signals could not. Here, the TCN is employed to extract features of normal ECG data. Then, our model is evaluated on an MIT-BIH arrhythmia dataset and CMUH dataset, with an accuracy, precision, recall, F1-score, and AUC of 0.9673, 0.9854, 0.9486, 0.9666, and 0.9672 and of 0.9358, 0.9816, 0.8882, 0.9325, and 0.9358, respectively. The result indicates that the ECG-AAE can detect abnormal ECG efficiently, with its performance better than other popular outlier detection methods.

show abstract

“…LSC determines outlier measure based on time increment for distance applied on the model. This method was improved in terms of computation in [37] by proposing method Fast LSC. In this approach, clustering is used to reduce the amount of data and hence proves ten times faster as compared to the LSC method.…”

Section: Liu Et Al [29] Introduce a Trajectory Outlier Detectionmentioning

confidence: 99%

A Novel Density-based Technique for Outlier Detection of High Dimensional Data Utilizing Full Feature Space

Rehman

Khan

2021

ITC

View full text Add to dashboard Cite

Recently, anomaly detection has acquired a realistic response from data mining scientists as a graph of its reputation has increased smoothly in various practical domains like product marketing, fraud detection, medical diagnosis, fault detection and so many other fields. High dimensional data subjected to outlier detection poses exceptional challenges for data mining experts and it is because of natural problems of the curse of dimensionality and resemblance of distant and adjoining points. Traditional algorithms and techniques were experimented on full feature space regarding outlier detection. Customary methodologies concentrate largely on low dimensional data and hence show ineffectiveness while discovering anomalies in a data set comprised of a high number of dimensions. It becomes a very difficult and tiresome job to dig out anomalies present in high dimensional data set when all subsets of projections need to be explored. All data points in high dimensional data behave like similar observations because of its intrinsic feature i.e., the distance between observations approaches to zero as the number of dimensions extends towards infinity. This research work proposes a novel technique that explores deviation among all data points and embeds its findings inside well established density-based techniques. This is a state of art technique as it gives a new breadth of research towards resolving inherent problems of high dimensional data where outliers reside within clusters having different densities. A high dimensional dataset from UCI Machine Learning Repository is chosen to test the proposed technique and then its results are compared with that of density-based techniques to evaluate its efficiency.

show abstract

Anomaly Detection Method Based on Fast Local Subspace Classifier

Cited by 6 publications

References 10 publications

Local Neighborhood-based Outlier Detection of High Dimensional Data using different Proximity Functions

Local Neighborhood-based Outlier Detection of High Dimensional Data using different Proximity Functions

Abnormal ECG detection based on an adversarial autoencoder

A Novel Density-based Technique for Outlier Detection of High Dimensional Data Utilizing Full Feature Space

Contact Info

Product

Resources

About