Handling class imbalance in direct marketing dataset using a hybrid data and algorithmic level solutions

al-Rifaie, Mohammad Majid; Alhakbani, Haya Abdullah

doi:10.1109/sai.2016.7556019

Cited by 11 publications

(8 citation statements)

References 17 publications

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“…In fact, the more the ML techniques have evolved over time, the more the schemes for spreading fake news have changed. • We apply re-sampling techniques, such as under-sampling and over-sampling, due to the class imbalance of the realworld dataset [14,15]. Disproportion between classes still represents an open issue and challenge for researchers focused on classification problems.…”

Section: Introductionmentioning

confidence: 99%

Detection of Fake News on COVID-19 on Web Search Engines

2021

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In early January 2020, after China reported the first cases of the new coronavirus (SARS-CoV-2) in the city of Wuhan, unreliable and not fully accurate information has started spreading faster than the virus itself. Alongside this pandemic, people have experienced a parallel infodemic, i.e., an overabundance of information, some of which is misleading or even harmful, which has widely spread around the globe. Although social media are increasingly being used as the information source, web search engines, such as Google or Yahoo!, still represent a powerful and trustworthy resource for finding information on the Web. This is due to their capability to capture the largest amount of information, helping users quickly identify the most relevant, useful, although not always the most reliable, results for their search queries. This study aims to detect potential misleading and fake contents by capturing and analysing textual information, which flow through search engines. By using a real-world dataset associated with recent COVID-19 pandemic, we first apply re-sampling techniques for class imbalance, and then we use existing machine learning algorithms for classification of not reliable news. By extracting lexical and host-based features of associated uniform resource locators (URLs) for news articles, we show that the proposed methods, so common in phishing and malicious URL detection, can improve the efficiency and performance of classifiers. Based on these findings, we suggest that the use of both textual and URL features can improve the effectiveness of fake news detection methods.

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Section: Introductionmentioning

confidence: 99%

Detection of Fake News on COVID-19 on Web Search Engines

2021

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“…HybridDA amalgamates SMOTE oversampling, random undersampling (RUS), and SVM optimization utilizing grid search [20]. This method combines both data level and algorithm level approaches, using the data level for generating samples and the algorithm level for optimization.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Sampling Approach for Improving the Classification of Imbalanced Data Using ROS and NCL Methods

2023

IJIES

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This research presents a novel hybrid sampling technique, implemented at the data level, to effectively address imbalanced and noisy data in classification processes. The proposed technique expertly combines two established methods, namely, the random over sampling (ROS) and neighbourhood cleaning rule (NCL) approaches, to tackle imbalance and noise issues, respectively. The study carried out an empirical evaluation of the proposed approach using crowdsourced text data that primarily emphasized the triple bottom line (TBL) dimension of a smart social, economic, and environmental city. The study used the long short-term memory (LSTM), convolutional neural networks (CNN), and CNN-LSTM classification models to validate the efficacy of the proposed hybrid sampling technique and compare its performance with other existing approaches, including ROS oversampling, NCL undersampling, synthetic minority over sampling & tomek links (SMOTE-Tomek), and synthetic minority oversampling and edited nearest neighbours (SMOTE-ENN) hybrid sampling. The results are impressive, with the ROS-NCL hybrid sampling technique achieving high accuracy rates across all three classification models, at 97.71%, 98.01%, and 98.11%, respectively. This approach provides a robust and effective solution for handling impure data and holds great promise in identifying complex data patterns in real-world classification problems.

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“…The class imbalance problem in direct marketing is usually solved by using one of the following three approaches: data-based approaches [7,8], algorithm-based approaches [9], or cost-based approaches [10]. Namely, the data-based approach balances classes using resampling techniques; algorithm-based solutions are based on specifically modified algorithms, while cost-based approaches allocate different misclassification costs to different class examples [11].…”

Section: Introductionmentioning

confidence: 99%

Customer Response Model in Direct Marketing: Solving the Problem of Unbalanced Dataset with a Balanced Support Vector Machine

Rogić

Kašćelan

Bach

2022

JTAER

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Customer response models have gained popularity due to their ability to significantly improve the likelihood of targeting the customers most likely to buy a product or a service. These models are built using databases of previous customers’ buying decisions. However, a smaller number of customers in these databases often bought the product or service than those who did not do so, resulting in unbalanced datasets. This problem is especially significant for online marketing campaigns when the class imbalance emerges due to many website sessions. Unbalanced datasets pose a specific challenge in data-mining modelling due to the inability of most of the algorithms to capture the characteristics of the classes that are unrepresented in the dataset. This paper proposes an approach based on a combination of random undersampling and Support Vector Machine (SVM) classification applied to the unbalanced dataset to create a Balanced SVM (B-SVM) data pre-processor resulting in a dataset that is analysed with several classifiers. The experiments indicate that using the B-SVM strategy combined with classification methods increases the base models’ predictive performance, indicating that the B-SVM approach efficiently pre-processes the data, correcting noise and class imbalance. Hence, companies may use the B-SVM approach to more efficiently select customers more likely to respond to a campaign.

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Handling class imbalance in direct marketing dataset using a hybrid data and algorithmic level solutions

Cited by 11 publications

References 17 publications

Detection of Fake News on COVID-19 on Web Search Engines

Detection of Fake News on COVID-19 on Web Search Engines

A Hybrid Sampling Approach for Improving the Classification of Imbalanced Data Using ROS and NCL Methods

Customer Response Model in Direct Marketing: Solving the Problem of Unbalanced Dataset with a Balanced Support Vector Machine

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