A hybrid data‐level sampling approach in learning from skewed user‐click data for click fraud detection in online advertising

Sisodia, Deepti; Sisodia, Dilip Singh

doi:10.1111/exsy.13147

Cited by 10 publications

(5 citation statements)

References 35 publications

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“…These redundant examples are far from the decision boundary removed by CNN. These methods ensure no loss of information and balance [18].…”

Section: One-sided Selection (Oss) Methodsmentioning

confidence: 99%

Arrhythmia Detection Based on New Multi-Model Technique for ECG Inter-Patient Classification

Oleiwi,

AlShemmary,

Al-Augby

2023

Int. J. Onl. Eng.

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This paper presents a novel model for arrhythmia detection based on a cascading technique that utilizes a combination of the One-Sided Selection (OSS) method, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms, this model denoted by (OWSK) model to classify four types of electrocardiogram (ECG) heartbeats following inter-patient scheme. The OWSK model consists of three stages. The first stage involves resampling using the One-Sided Selection (OSS) method to solve the imbalance problem and reduce data by removing noisy, borderline, and redundant samples. The second stage involves using Wavelet Transformation (WT) and Power Spectral Density (PSD) to extract the most relevant frequency domain features. The third stage involves a cascading process by constructing the classifier from SVM trained on the whole dataset to classify normal and abnormal beats. Then, KNN (K-Nearest Neighbors) is trained on only the three irregular minority classes to classify the three types of arrhythmias for the detection of ventricular ectopic beats, supraventricular ectopic beats, and fusion beats (V, S, and F). The performance of the proposed model is evaluated in terms of different metrics, including accuracy, recall, precision, and F1 score. The results show the superiority of the proposed model in medical diagnosis compared to the latest works, where it achieves 90%, 90%, 93%, and 91% for accuracy, recall, precision, and F1 score under the inter-patient paradigm and 98%, 98%, 98%, and 98% under the intra-patient paradigm.

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“…These redundant examples are far from the decision boundary removed by CNN. These methods ensure no loss of information and balance [18].…”

Section: One-sided Selection (Oss) Methodsmentioning

confidence: 99%

Arrhythmia Detection Based on New Multi-Model Technique for ECG Inter-Patient Classification

Oleiwi,

AlShemmary,

Al-Augby

2023

Int. J. Onl. Eng.

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“…Data preprocessing techniques, including resampling, oversampling, and undersampling, are employed to address this imbalance [60]. Additionally, transfer learning, where a model pre-trained on a large dataset is fine-tuned for fraud detection, can enhance performance [61]. Training strategies also involve the use of autoencoders for unsupervised feature learning and anomaly detection [62], enabling the model to identify patterns indicative of fraud without explicit labels.…”

Section: Deep Learning Approaches To Fraud Detectionmentioning

confidence: 99%

Artificial Intelligence Techniques for Fraud Detection

Lai

2023

Preprint

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In the wake of increasing digital fraud, this paper introduces an innovative application of Artificial Intelligence (AI) in detecting fraudulent activities across finance, healthcare, and e-commerce sectors. It presents a detailed analysis of machine learning methodologies, specifically focusing on the advantages of supervised, unsupervised, and deep learning techniques. The paper addresses the challenges such as data imbalance, model interpretability, and ethical implications in AI-based fraud detection. It also discusses the necessity of high-quality datasets and advocates for the integration of traditional and advanced machine learning methods to enhance accuracy and adaptability in fraud identification. However, it acknowledges the limitations including computational demands and overfitting risks. The study underscores the importance of collaborative efforts between AI experts and industry professionals to develop ethical, efficient, and reliable AI solutions for fraud detection.

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“…Therefore, the objective of this work is to prove that the usage of the correct image, correct keywords, and correct Meta features associated with the content can have a great impact on users' engagements-Views, shares, popularity of any article/blog/advertisement. This technique can avoid usage of user click fraud in online advertising [10], [11].…”

Section: Introductionmentioning

confidence: 99%

A Novel Multimodal Online News Popularity Prediction Model based on Ensemble Learning

Hassija

Arora

Bansal

et al. 2023

Preprint

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The prediction of news popularity is having substantial importance for the digital advertisement community in terms of selecting and engaging users. Traditional approaches are based on empirical data collected through surveys and applied statistical measures to prove a hypothesis. However, predicting news popularity based on statistical measures applied to past data is highly questionable. Therefore, in this paper, we predict news popularity using machine learning classification models and deep residual neural network models. Articles are usually made up of textual content and in many cases, images are also used. Although it is evident that the appropriate amount of textual data is required to extract features and create models, image data is also helpful in gaining useful information. In this paper, we present a novel multimodal online news popularity prediction model based on ensemble learning. This research work acts as a guide for extensive feature engineering, feature extraction, feature selection, and effective modeling to create a robust news popularity Prediction Model. Three kinds of features – meta features, text features, and image features are used to design an influential and robust model. The performance measure Root Mean Squared logarithmic error (RMSLE) is used to validate the outcome of the proposed model. Further, the most important features are sought out for the proposed model to verify the dependence of the model on text and image features.

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A hybrid data‐level sampling approach in learning from skewed user‐click data for click fraud detection in online advertising

Cited by 10 publications

References 35 publications

Arrhythmia Detection Based on New Multi-Model Technique for ECG Inter-Patient Classification

Arrhythmia Detection Based on New Multi-Model Technique for ECG Inter-Patient Classification

Artificial Intelligence Techniques for Fraud Detection

A Novel Multimodal Online News Popularity Prediction Model based on Ensemble Learning

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