A Fuzzy Rule-Based Framework for Consumer Behavior Analytics

doi:10.1177/24056456251385734

Return

A Fuzzy Rule-Based Framework for Consumer Behavior Analytics

2025-10-01

PRE

Sasidhar Kothuru *

Rajesh S. Kulkarni

DOI：10.1177/24056456251385734

Original

Original request for help

Save

Abstract

En 中文

Understanding consumer behavior is vital for businesses seeking to personalize services, optimize marketing strategies, and improve customer retention. However, analyzing such behavior at scale presents significant challenges due to the volume, velocity, and variety of data, as well as the need for accurate and interpretable prediction models. Traditional classification methods often fall short when applied to large-scale, high-dimensional behavioral datasets, leading to issues in scalability, accuracy, and real-time processing. To address these limitations, this paper introduces a novel framework for consumer behavior analysis using an Improved Fuzzy Classification with Bagging and MapReduce Coordination (IFCBMC) approach, specifically designed for big data environments. The primary objectives of this research are: (1) to develop a scalable classification model suitable for distributed data processing, (2) to enhance prediction accuracy through fuzzy rule-based learning, and (3) to evaluate the robustness of the proposed model against existing state-of-the-art classifiers. The process begins with data preprocessing, including cleaning and modified normalization, followed by distribution of the data across a MapReduce architecture to manage scale and speed. Extracted features from multiple data partitions (mappers) are aggregated and processed by an enhanced fuzzy rule-based classification model. To improve prediction robustness, a bagging ensemble strategy is applied, where multiple classifiers are trained on different data subsets, and the best-performing models are randomly selected and merged during the reduce phase. The proposed IFCBMC method outperforms all compared models, achieving the highest accuracy of 0.960, significantly surpassing traditional approaches such as LSTM (0.862), LINKNET (0.866), SQUEEZENET (0.858), SVM (0.863), DCNN (0.860), Bi-GRU (0.854), RNN (0.861), and DNN (0.860).

Keywords:

modified normalization

pre-processing MapReduce framework

sensitivity

data cleaning