Abstract
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.
Highlights
With the increasing accessibility of innumerable data collections, the extraction of interesting patterns from such data becomes a necessity
The first step commences with generating the prediction model using the “training” dataset that comprises a set of samples, where each sample is associated with a categorical class label
The classification problems can be differentiated according to: (i) the number of class labels featured in the dataset and (ii) the number of the class labels associated with each sample in the dataset
Summary
With the increasing accessibility of innumerable data collections, the extraction of interesting patterns from such data becomes a necessity. The considered dataset includes only two labels, while more than two labels featured in the multi-class classification problems. The last step in the classification process is the model usage, where the prediction model is utilized to predict class labels for new unseen data. Classification has been employed in many application domains, examples of application domains include: text categorization [4], bioinformatics [5], manufacturing [6], e-learning evaluation system [7], medical diagnosis [8], data management [9], music categorization [10] and movie genre prediction [11] Among these music categorization and movie genre predictions or genre preferences prediction [12], [13] could be considered as entertainment applications of classification. Several algorithms are available for this purpose, the most vastly used algorithms are:
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