Work2019

A data modeling approach for classification problems

Stéphane Cédric Koumetio Tekouabou · Walid Cherif · Hassan Silkan

13 citations0/0 files storedInternational Conference on Networking, Information Systems & Security (NISS)

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Customer churn and segmentation Data Mining and Machine Learning Applications Financial Distress and Bankruptcy Prediction

Abstract

In this paper, we present a new data modeling approach for five common classification algorithms to optimize the prediction of telemarketing target calls for selling bank long-term deposits. A Portuguese retail bank addressed, from 2008 until 2013, data on its clients, products and social-economic attributes including the effects of the financial crisis. An original set of 150 features has been explored and 21 features are retained for the proposed approach including label. This paper introduces a new modeling approach that preprocessed separately each type of features and normalize them to optimize prediction performance. To evaluate the proposed approach, this paper compares the results obtained with five most known machine learning techniques: Naïve Bayes (NB), Logistic Regression (LR), Decision Trees (DT), Artificial Neural Network (ANN) and Support Vector Machines (SVM) and it yielded better improved performances for all these algorithms in terms of accuracy and f-measure.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 13 citations exist upstream.

References

2008Application of data mining techniques in customer relationship management: A literature review and classification 2014A data-driven approach to predict the success of bank telemarketing 2011Using data mining for bank direct marketing: an application of the CRISP-DM methodology 2005Linking innovative product development with customer knowledge: a data-mining approach 2015An Efficient CRM-Data Mining Framework for the Prediction of Customer Behaviour 2014Bank Direct Marketing Analysis of Data Mining Techniques 2014Using customer lifetime value and neural networks to improve the prediction of bank deposit subscription in telemarketing campaigns 2017Predicting direct marketing response in banking: comparison of class imbalance methods

Cited by

2021Improving KNN Model for Direct Marketing Prediction in Smart Cities 2022Predicting Customer’s Subscription Response to Bank Telemarketing Campaign Based on Machine learning Algorithms

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.