A Comparative Analysis of Artificial Neural Networks and Logistic Regression

Naseri, Mohammad Bakher; Elliott, Greg

doi:10.3166/jds.19.291-312

Cited by 4 publications

(3 citation statements)

References 51 publications

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“…Telemarketing campaigns become the preferred mean among banking sector to promote products or services being offered [10,27]. Due to its prevalence, ANN become the preferred classification algorithm to handle complex finance and marketing issues [14,22,23,28,31,36].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Researchers and practitioners in the field of electronic commerce have been striving to streamline and promote various business processes. For a few decades, different interesting research efforts have attempted to improve understanding of the behavior of customers using ANNs [1,14,[22][23][24]26,27,31]. However, cost-sensitive algorithms have been with marginal interest to the researchers in bank marketing, while pre-processing the input dataset by re-sampling techniques to solve imbalanced class distribution have gained significant interest [1,15].…”

Section: Introductionmentioning

confidence: 99%

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Business Analytics in Telemarketing: Cost-Sensitive Analysis of Bank Campaigns Using Artificial Neural Networks

et al. 2020

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The banking industry has been seeking novel ways to leverage database marketing efficiency. However, the nature of bank marketing data hindered the researchers in the process of finding a reliable analytical scheme. Various studies have attempted to improve the performance of Artificial Neural Networks in predicting clients’ intentions but did not resolve the issue of imbalanced data. This research aims at improving the performance of predicting the willingness of bank clients to apply for a term deposit in highly imbalanced datasets. It proposes enhanced Artificial Neural Network models (i.e., cost-sensitive) to mitigate the dramatic effects of highly imbalanced data, without distorting the original data samples. The generated models are evaluated, validated, and consequently compared to different machine-learning models. A real-world telemarketing dataset from a Portuguese bank is used in all the experiments. The best prediction model achieved 79% of geometric mean, and misclassification errors were minimized to 0.192, 0.229 of Type I & Type II Errors, respectively. In summary, an interesting Meta-Cost method improved the performance of the prediction model without imposing significant processing overhead or altering original data samples.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Business Analytics in Telemarketing: Cost-Sensitive Analysis of Bank Campaigns Using Artificial Neural Networks

et al. 2020

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“…This study employed an Artificial Neural Network (ANN) model to assess the key factors impacting the sustainability of small-scale beef farms. While previous research identified significant predictive inputs with acceptable accuracy (Dreiseitl & Ohno-Machado, 2002;Naseri & Elliott, 2010), the specific contributions of individual parameters within ANN models remain largely unexplored (Gevrey et al, 2003;Olden et al, 2004). Therefore, a logistic regression model was also applied in parallel.…”

Section: Discussionmentioning

confidence: 99%

Factors Influencing Productivity and Sustainability of Small-Scale Beef Farms in Thailand

Panbamrungkij,

Inchaisri,

Phan-iam

et al. 2023

ABAC J

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Over the past few decades, Thailand has witnessed a decline in production from small-scale beef cattle farming, accompanied by a reduced interest from younger generations in continuing family farming. The present study aimed to determine the state of these farms based on good agricultural practices (GAP) and explored factors influencing beef herd productivity and the willingness of the next generation to sustain the family business. Data from 1,175 questionnaires from 34 provinces was analyzed using SPSS. Beef herds with higher calving rates (CR ≥ 60%) had better herd, reproductive, and health management scores compared with the lower CR (<60%) group. Results from a negative binomial regression showed that factors, i.e., prediction of calving date and routine deworming positively impacted CR, while others like feeding types, mineral supplements, vaccination and hormonal estrus synchronization negatively correlated with CR. Expert opinions (N = 83) highlighted the importance of recording systems, heat detection programs, pregnancy diagnosis, feeding type, sufficient qualified water supply and mineral supplements. The use of Artificial Neural Network (ANN) and GLM analysis also noted that farm potential score, farmer gender, and occupations were pivotal for the sustainability of beef farming. This research aids in strategizing for enhanced sustainable beef production in Thailand.

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Marketing Meets Data Science: Bridging the Gap

Moscato

Vries

2019

Business and Consumer Analytics: New Ideas

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A Comparative Analysis of Artificial Neural Networks and Logistic Regression

Cited by 4 publications

References 51 publications

Business Analytics in Telemarketing: Cost-Sensitive Analysis of Bank Campaigns Using Artificial Neural Networks

Business Analytics in Telemarketing: Cost-Sensitive Analysis of Bank Campaigns Using Artificial Neural Networks

Factors Influencing Productivity and Sustainability of Small-Scale Beef Farms in Thailand

Marketing Meets Data Science: Bridging the Gap

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