A comparative study and performance analysis of multirelational classification algorithms. (8th February 2022) - Shah, Komal; Patel, Kajal S.
Classification is one of the important tasks in data mining in which a model is generated-based on training dataset and that model is used to predict class label of unknown dataset. Many propositional classification algorithms exist to build accurate and scalable classifiers, applied to single table dataset only. Most real-world data are structured and stored in relational format and single table classification algorithms that cannot deal directly with relational data. Hence, the need for a multirelational classification algorithm that learns relational data and predicts class labels for relational tuple arises. For relational classification, various techniques are available that include flattening relational data, upgrading existing algorithm, and multiview learning. This paper presents comparative analysis of these techniques and algorithms in detail and shows that multiview-based algorithms outperform other algorithms. By implementing multiview-based algorithms it demonstrated that these algorithms achieve higher accuracy for binary class classification than multiclass classification.