A General Multi-relational Classification Approach Using Feature Generation and Selection

摘要：

Abstract Multi-relational classification is an important data mining task, since much real world data is organized in multiple relations. The major challenges come from, firstly, the large high dimensional search spaces due to many attributes in multiple relations and, secondly, the high computational cost in feature selection and classifier construction due to the high complexity in the structure of multiple relations. The existing approaches mainly use the inductive logic programming (ELP) techniques to derive hypotheses or extract features for classification. However, those methods often are slow and sometimes cannot provide enough information to build effective classifiers. In this paper, we develop a general approach for accurate and fast multi-relational classification using feature generation and selection. Moreover, we propose a novel similarity-based feature selection method for multi-relational classification. An extensive performance study on several benchmark data sets indicates that our approach is accurate, fast and highly scalable.

关键词： Multi-relational classification Feature generation Feature selection

作者: Miao Zou Tengjiao Wang Hongyan Li Dongqing Yang

作者单位: School of Electronics Engineering and Computer Science, Peking University,Beijing 100871 China

会议类型: 国际会议

会议名称: 6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)

会议地点: 重庆

会议语种:英文

页码: 21-33

在线出版日期: 2010-11-19（万方平台首次上网日期，不代表论文的发表时间）