International Conference on Computer and Knowledge Engineering

Home / 15th International Conference on Computer and Knowledge Engineering

Uncertainty-Aware Deep Ensembles for Confident Customer Churn Prediction with Rejection Option

Authors :

Fatemeh Moradi¹ Mehran Tarif² Mohammadhossein Homaei³

1- Faculty of Engineering Isfahan (Khorasgan) Branch, Islamic Azad University Isfahan, Iran 2- Department of Computer Science University of Verona Verona, Italy 3- Media Engineering Group University of Extremadura C´aceres, Spain

Keywords :

Customer churn prediction،uncertainty quantification،deep ensembles،rejection option،Monte Carlo dropout،Bayesian neural networks

Abstract :

Customer churn prediction is a key challenge in business intelligence, especially in industries where retaining clients is costly and vital. Most prediction models achieve high accuracy but fail to express how confident their predictions are, which can lead to expensive or misguided interventions. To address this gap, this paper proposes an Uncertainty-Aware Ensemble (UA-Ensemble) framework that quantifies prediction confidence alongside churn prediction. It combines five distinct neural network architectures, including attention-based LSTMs, Bayesian networks, and Monte Carlo Dropout, to capture both aleatoric and epistemic uncertainty. The framework incorporates a cost-aware rejection mechanism to abstain from acting on unreliable predictions. Experiments on large-scale datasets from banking, e-commerce, and telecom sectors achieved 94.2% accuracy with well-calibrated uncertainty estimates and 18.3% reduction in intervention costs compared to baseline methods. The approach outperforms existing models including traditional machine learning methods, deep learning baselines, and alternative uncertainty quantification techniques, demonstrating effectiveness across different industries and data scales, making it a practical tool for trustworthy business decision-making.

Papers List

List of archived papers

Adaptive Sliding Window Optimization for Multi-Dimensional Data Streams Using Reinforcement Learning

Abolfazl Zarghani

Zone-Based Federated Learning in Indoor Positioning

Omid Tasbaz - Vahideh Moghtadaiee - Bahar Farahani

Enhanced Melanoma Detection: An Improved Deformable DETR Model with Efficient Channel Attention

Amirreza Rouhbakhshmeghrazi - Shayan Nalbandian - Sheida Shadman - Mohammad Reza Hassannezhad - Shuyuan Yang - Bo Li

Virus-Antiviral Prediction Using Machine and Deep Learning Methods

Shayan Majidifar - Fatemeh Nasiri - Mohsen Hooshmand

Towards Transparent and Accurate Story Point Estimation via Interpretable BERT-based Modeling

Seyed Emad Baradaran Hosseini - Maryam Khodabakhsh - Alireza Tajary - Seyedehfatemeh Karimi

Towards Efficient Video Object Detection on Embedded Devices

Mohammad Hajizadeh - Adel Rahmani - Mohammad Sabokrou

An Automated Visual Defect Segmentation for Flat Steel Surface Using Deep Neural Networks

Dorna Nourbakhsh Sabet - Mohammad Reza Zarifi - Javad Khoramdel - Yasamin Borhani - Esmaeil Najafi

Brain Age Estimation with Twin Vision Transformer using Hippocampus Information Applicable to Alzheimer Dementia Diagnosis

Zahra Qodrati - Seyedeh Masoumeh Taji - Amirhossein Ghaemi - Habibollah Danyali - Kamran Kazemi - Alireza Ghaemi

A Language-Independent Approach to Classification of Textual File Fragments: Case Study of Persian, English, and Chinese Languages

Fatemeh Mansouri Hanis - Hamidreza Khoshvaghti - Mehdi Teimouri - Hadi Veisi

Performance Evaluation Study of Color Space Selection In Video Based Facial Expression Recognition Using Deep Neural Networks For Sentiment Analysis

Phee Wei Qin - Ervin Gubin Moung - Ali Farzamnia - Farashazillah Yahya - John Julius Danker Khoo - Maisarah Mohd Sufian