Multi-relational classification is highly challengeable task in data mining, because so much data in our world is organised in multiple relations. The challenge comes from the huge collection of search spaces and high calculation cost arises in the selection of feature due to excessive complexity in the various relations. The state-of-the-art approach is based on clusters and inductive logical programming to retrieve important features and derived hypothesis. However, those techniques are very slow and unable to create enough data and information to produce efficient classifiers. In the given paper, we proposed a fast and effective method for the feature selection using multi-relational classification. Moreover we introduced the natural join and SVM based feature selection in multi-relation statistical learning. The performance of our model on various datasets indicates that our model is efficient, reliable and highly accurate.

中文翻译：

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使用ML的多关系统计建模中特征生成和选择的新改进方法

在我们的数据挖掘中，多重关系分类是一项极富挑战性的任务，因为我们世界上有那么多数据是以多重关系组织的。挑战来自巨大的搜索空间集合，并且由于各种关系的过度复杂性，在特征选择中产生了很高的计算成本。最先进的方法基于聚类和归纳逻辑编程，以检索重要特征和派生的假设。但是，这些技术非常缓慢，无法创建足够的数据和信息来产生有效的分类器。在本文中，我们提出了一种使用多关系分类的快速有效的特征选择方法。此外，我们在多关系统计学习中引入了自然联接和基于SVM的特征选择。