Feature selection with data balancing for prediction of bank telemarketing

Vajiramedhin, Chakarin; Suebsing, Anirut

doi:10.12988/ams.2014.47222

Cited by 19 publications

(22 citation statements)

References 6 publications

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“…The bank direct marketing data sets from Portuguese banking institutions have been popular data resources and were investigated in [57][58][59] which compared the performances of four different DM classification techniques. The same dataset was used for verifying the proposed approach in [60] that employed correlation-based feature subsection selection algorithm along with the data set balancing technique. Later, this model was developed by [61] with an ensemble framework.…”

Section: Customer Development and Customizationmentioning

confidence: 99%

Digitalisation and Big Data Mining in Banking

Hassani

Huang

Silva

2018

BDCC

120

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Banking as a data intensive subject has been progressing continuously under the promoting influences of the era of big data. Exploring the advanced big data analytic tools like Data Mining (DM) techniques is key for the banking sector, which aims to reveal valuable information from the overwhelming volume of data and achieve better strategic management and customer satisfaction. In order to provide sound direction for the future research and development, a comprehensive and most up to date review of the current research status of DM in banking will be extremely beneficial.Since existing reviews only cover the applications until 2013, this paper aims to fill this research gap and presents the significant progressions and most recent DM implementations in banking post 2013. By collecting and analyzing the trends of research focus, data resources, technological aids, and data analytical tools, this paper contributes to bringing valuable insights with regard to the future developments of both DM and the banking sector along with a comprehensive one stop reference table. Moreover, we identify the key obstacles and present a summary for all interested parties that are facing the challenges of big data.

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Section: Customer Development and Customizationmentioning

confidence: 99%

Digitalisation and Big Data Mining in Banking

Hassani

Huang

Silva

2018

BDCC

120

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“…Such dataset was studied by numerous scholars and researchers, as the high number of page hits shows, above five hundred thousand. As a result, several studies have been published using its data, with the most for assessing machine learning and DM algorithms' capabilities [41], and a few for feature selection [42]. This dataset encompasses a total of 41,188 phone contacts conducted by human agents from a Portuguese bank between 2008 and 2010, with the goal of selling an attractive long-term deposit, in an attempt of retaining customers' financial assets in the institution.…”

Section: Bank Telemarketing Case Studymentioning

confidence: 99%

Mutual information and sensitivity analysis for feature selection in customer targeting: A comparative study

Barraza

Moro

Ferreyra³

et al. 2018

Journal of Information Science

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Feature selection is a highly relevant task in any data-driven knowledge discovery project. The present research focus on analysing the advantages and disadvantages of using mutual information (MI) and data-based sensitivity analysis (DSA) for feature selection in classification problems, by applying both to a bank telemarketing case. A logistic regression model is built on the tuned set of features identified by each of the two techniques as the most influencing set of features on the success of a telemarketing contact, in a total of 13 features for MI and 9 for DSA. The latter performs better for lower values of false positives while the former is slightly better for a higher false positive ratio. Thus, MI becomes a better choice if the intention is reducing slightly the cost of contacts without risking losing a high number of successes. On the other side, DSA achieved good prediction results with less features.

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“…Vajiramedhin and Suebsing [30] proposed a model on the same dataset, focusing on using correlation-based feature subset selection algorithm and a dataset balancing technique. The balancing technique is used to make the dataset label equivalent by randomly selecting the dataset of each label equally.…”

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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Feature selection with data balancing for prediction of bank telemarketing

Cited by 19 publications

References 6 publications

Digitalisation and Big Data Mining in Banking

Digitalisation and Big Data Mining in Banking

Mutual information and sensitivity analysis for feature selection in customer targeting: A comparative study

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

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