A High-Performance Customer Churn Prediction System Based on Self-Attention

نویسندگان

چکیده

Customer churn prediction is a challenging domain of research that contributes to customer retention strategy. The predictive performance existing machine learning models, which are often adopted by communities, appear be at bottleneck, partly due models' poor feature extraction capability. Therefore, novel algorithm, hybrid neural network with self-attention enhancement (HNNSAE), proposed in this paper improve the efficiency screening and extraction, consequently improving model's performance. This model consists three main blocks. first block entity embedding layer, employed process categorical variables transformed into 0-1 code. second extractor, extracts significant features through multi-head mechanism. In addition, effect, we stack residual connection on modules. third classifier, three-layer multilayer perceptron. work conducts experiments publicly available dataset related commercial bank customers. result demonstrates HNNSAE significantly outperforms other Individual Machine Learning (IML), Ensemble (EML), Deep (DL) methods tested paper. Furthermore, compare extractor extractors find method methods. four hypotheses about overfitting risk dataset.

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ژورنال

عنوان ژورنال: Social Science Research Network

سال: 2022

ISSN: ['1556-5068']

DOI: https://doi.org/10.2139/ssrn.4145486