- author = "Pedro Jose Pereira and Paulo Cortez and Rui Mendes",
- title = "Multi-objective Grammatical Evolution of Decision Trees for Mobile Marketing user conversion prediction",
- journal = "Expert Systems with Applications",
- year = "2021",
- volume = "168",
- pages = "114287",
- month = "15 " # apr,
- keywords = "genetic algorithms, genetic programming, Grammatical Evolution, MOGP, MOGA, Conversion Rate (CVR) prediction, Decision Trees, Explainable Artificial Intelligence, XAI, Lamarckian Evolution, PonyGE2",
- ISSN = "0957-4174",
-
URL = "
https://www.sciencedirect.com/science/article/pii/S0957417420309891",
-
code_url = "https://pypi.org/project/cane/",
-
DOI = "
doi:10.1016/j.eswa.2020.114287",
- size = "12 pages",
- abstract = "The worldwide adoption of mobile devices is raising the value of Mobile Performance Marketing, which is supported by Demand-Side Platforms (DSP) that match mobile users to advertisements. In these markets, monetary compensation only occurs when there is a user conversion. Thus, a key DSP issue is the design of a data-driven model to predict user conversion. To handle this nontrivial task, we propose a novel Multi-objective Optimization (MO) approach to evolve Decision Trees (DT) using a Grammatical Evolution (GE), under two main variants: a pure GE method (MGEDT) and a GE with Lamarckian Evolution (MGEDTL). Both variants evolve variable-length DTs and perform a simultaneous optimization of the predictive performance and model complexity. To handle big data, the GE methods include a training sampling and parallelism evaluation mechanism. The algorithms were applied to a recent database with around 6 million records from a real-world DSP. Using a realistic Rolling Window (RW) validation, the two GE variants were compared with a standard DT algorithm (CART), a Random Forest and a state-of-the-art Deep Learning (DL) model. Competitive results were obtained by the GE methods, which present affordable training times and very fast predictive response times.",
-
notes = "ALGORITMI Centre, Department of Information Systems,
University of Minho, 4804-533 Guimaraes, Portugal
Also known as \cite{PEREIRA2021114287}",
