Bank Direct Marketing Analysis of Data Mining Techniques

Elsalamony, Hany A.

doi:10.5120/14852-3218

Cited by 52 publications

(33 citation statements)

References 15 publications

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“…To estimate the parameters (means and variances of the variables) necessary for classification, the classifier requires only a small amount of training data. It also handles real and discrete data [22].…”

Section: A Logistic Regressionmentioning

confidence: 99%

Prediction of Employee Turnover in Organizations using Machine Learning Algorithms

Punnoose¹,

Ajit²

2016

ijarai

125

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Abstract-Employee turnover has been identified as a key issue for organizations because of its adverse impact on work place productivity and long term growth strategies. To solve this problem, organizations use machine learning techniques to predict employee turnover. Accurate predictions enable organizations to take action for retention or succession planning of employees. However, the data for this modeling problem comes from HR Information Systems (HRIS); these are typically under-funded compared to the Information Systems of other domains in the organization which are directly related to its priorities. This leads to the prevalence of noise in the data that renders predictive models prone to over-fitting and hence inaccurate. This is the key challenge that is the focus of this paper, and one that has not been addressed historically. The novel contribution of this paper is to explore the application of Extreme Gradient Boosting (XGBoost) technique which is more robust because of its regularization formulation. Data from the HRIS of a global retailer is used to compare XGBoost against six historically used supervised classifiers and demonstrate its significantly higher accuracy for predicting employee turnover.

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Section: A Logistic Regressionmentioning

confidence: 99%

Prediction of Employee Turnover in Organizations using Machine Learning Algorithms

Punnoose¹,

Ajit²

2016

ijarai

125

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“…Repository. This dataset are collected and arranged by [33] and also utilized by [34]. Our proposed hybrid approach used this dataset to determine its performances and resolves the privacy issues.…”

Section: Data Collection Procedures and Publishing On Cloudmentioning

confidence: 99%

An innovative privacy preserving technique for incremental datasets on cloud computing

Aldeen

Salleh

Aljeroudi

2016

Journal of Biomedical Informatics

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Cloud computing (CC) is a magnificent service-based delivery with gigantic computer processing power and data storage across connected communications channels. It imparted overwhelming technological impetus in the internet (web) mediated IT industry, where users can easily share private data for further analysis and mining. Furthermore, user affable CC services enable to deploy sundry applications economically. Meanwhile, simple data sharing impelled various phishing attacks and malware assisted security threats. Some privacy sensitive applications like health services on cloud that are built with several economic and operational benefits necessitate enhanced security. Thus, absolute cyberspace security and mitigation against phishing blitz became mandatory to protect overall data privacy. Typically, diverse applications datasets are anonymized with better privacy to owners without providing all secrecy requirements to the newly added records. Some proposed techniques emphasized this issue by re-anonymizing the datasets from the scratch. The utmost privacy protection over incremental datasets on CC is far from being achieved. Certainly, the distribution of huge datasets volume across multiple storage nodes limits the privacy preservation. In this view, we propose a new anonymization technique to attain better privacy protection with high data utility over distributed and incremental datasets on CC. The proficiency of data privacy preservation and improved confidentiality requirements is demonstrated through performance evaluation.

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“…In another piece of research, Elsalamony [32] focused on increasing the effectiveness of the marketing campaign by finding the major attributes that affected the success of the phone call. The author compared three classification methods: multilayer perception neural network (MLPNN), Bayesian networks, logistic regression (LR), and C5.0 on the direct marketing dataset and found that C5.0 gave the best results, with the testing part scoring:…”

Section: Comparison With Other Techniquesmentioning

confidence: 99%

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

al-Rifaie

Alhakbani

2016

2016 SAI Computing Conference (SAI)

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Abstract-Class imbalance is a major problem in machine learning. It occurs when the number of instances in the majority class is significantly more than the number of instances in the minority class. This is a common problem which is recurring in most datasets, including the one used in this paper (i.e. direct marketing dataset). In direct marketing, businesses are interested in identifying potential buyers, or charities wish to identify potential givers. Several solutions have been suggested in the literature to address this problem, amongst which are datalevel techniques, algorithmic-level techniques and a combination of both.In this paper, a model is proposed to solve imbalanced data using a Hybrid of Data-level and Algorithmic-level solutions (HybridDA), which involves oversampling the minority class, undersampling the majority class, and additionally, optimising the cost parameter, the gamma and the kernel type of Support Vector Machines (SVM) using a grid search. The proposed model perfomed competitively compared with other models on the same dataset. The dataset used in this work are real-world data collected from a Portuguese marketing campaign for bankdeposit subscriptions and are available from the University of California, Irvine (UCI) Machine Learning Repository.

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Bank Direct Marketing Analysis of Data Mining Techniques

Cited by 52 publications

References 15 publications

Prediction of Employee Turnover in Organizations using Machine Learning Algorithms

Prediction of Employee Turnover in Organizations using Machine Learning Algorithms

An innovative privacy preserving technique for incremental datasets on cloud computing

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

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