Single and Ensemble Classification for Predicting User’s Restaurant Preference

Classification is one of the most attractive and powerful data mining functionalities. Classification algorithms are applied to real-world problems to produce intelligent prediction models. Two main categories of classification algorithms can be adopted for generating prediction models: Single and Ensemble classification algorithms. In this paper, both categories are utilized to generate a novel prediction model to predict restaurant category preferences. More specifically, the central idea espoused in this paper is to construct an effective prediction model, using Single and Ensemble classification algorithms, to assist people to determine the best relevant place to go based on their demographic data, income level and place preferences. Therefore, this paper introduces a new application of classification task. According to the reported experimental results, an effective Restaurant Category Preferences Prediction Model (RCPPM) could be generated using classification algorithms. In addition, Bagging Homogeneous Ensemble classification produced the most effective RCPPM.

@article{Alshdaifat2020,
  title     = {Single and Ensemble Classification for Predicting User’s Restaurant Preference},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {7},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Esra’a Alshdaifat and Ala’a Al-shdaifat},
  doi       = {10.14569/IJACSA.2020.0110782},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110782}
}