Outlier Detection in High Dimensional Data

Kamalov, Firuz; Leung, Ho-Hon

doi:10.1142/s0219649220400134

Cited by 30 publications

(15 citation statements)

References 30 publications

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“…In this paper, we employ a kernel density-based method to identify the outliers in a dataset. Kernel density estimation (KDE) is a well-established statistical method that is widely used in different applications [ 41 ]. It is a nonparametric technique for estimating the underlying distribution of the sample data using a kernel function.…”

Section: Methodsmentioning

confidence: 99%

Machine learning based approach to exam cheating detection

2021

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The COVID-19 pandemic has impelled the majority of schools and universities around the world to switch to remote teaching. One of the greatest challenges in online education is preserving the academic integrity of student assessments. The lack of direct supervision by instructors during final examinations poses a significant risk of academic misconduct. In this paper, we propose a new approach to detecting potential cases of cheating on the final exam using machine learning techniques. We treat the issue of identifying the potential cases of cheating as an outlier detection problem. We use students’ continuous assessment results to identify abnormal scores on the final exam. However, unlike a standard outlier detection task in machine learning, the student assessment data requires us to consider its sequential nature. We address this issue by applying recurrent neural networks together with anomaly detection algorithms. Numerical experiments on a range of datasets show that the proposed method achieves a remarkably high level of accuracy in detecting cases of cheating on the exam. We believe that the proposed method would be an effective tool for academics and administrators interested in preserving the academic integrity of course assessments.

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Section: Methodsmentioning

confidence: 99%

Machine learning based approach to exam cheating detection

2021

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“…KDE is a well-known nonparametric density estimator used in number of applications [7], [9]. The KDE-based sampling approach is based on estimating the underlying distribution of the minority data.…”

Section: Methodology a Sampling Methodsmentioning

confidence: 99%

Kernel density estimation-based sampling for neural network classification

Kamalov¹,

Elnagar²

2021

Preprint

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Imbalanced data occurs in a wide range of scenarios. The skewed distribution of the target variable elicits bias in machine learning algorithms. One of the popular methods to combat imbalanced data is to artificially balance the data through resampling. In this paper, we compare the efficacy of a recently proposed kernel density estimation (KDE) sampling technique in the context of artificial neural networks. We benchmark the KDE sampling method against two base sampling techniques and perform comparative experiments using 8 datasets and 3 neural networks architectures. The results show that KDE sampling produces the best performance on 6 out of 8 datasets. However, it must be used with caution on image datasets. We conclude that KDE sampling is capable of significantly improving the performance of neural networks.

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“…A common approach to address class imbalance is to balance the data through resampling [5]. Alternatively, outlier detection methods can be used in place of the standard classification algorithms [6].…”

Section: Literaturementioning

confidence: 99%

Forecasting with Deep Learning: S&P 500 index

Kamalov

Smail

Gurrib

2020

2020 13th International Symposium on Computational Intelligence and Design (ISCID)

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Stock price prediction has been the focus of a large amount of research but an acceptable solution has so far escaped academics. Recent advances in deep learning have motivated researchers to apply neural networks to stock prediction. In this paper, we propose a convolution-based neural network model for predicting the future value of the S&P 500 index. The proposed model is capable of predicting the next-day direction of the index based on the previous values of the index. Experiments show that our model outperforms a number of benchmarks achieving an accuracy rate of over 55%.

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Outlier Detection in High Dimensional Data

Cited by 30 publications

References 30 publications

Machine learning based approach to exam cheating detection

Machine learning based approach to exam cheating detection

Kernel density estimation-based sampling for neural network classification

Forecasting with Deep Learning: S&P 500 index

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