05961119

a

05961119 Liu, Huawen Li, Minshuo Zhao, Jianmin Mo, Yuchang An effective feature selection method using dynamic information criterion. Deng, Hepu (ed.) et al., Artificial intelligence and computational intelligence. Third international conference, AICI 2011, Taiyuan, China, September 24--25, 2011. Proceedings, Part I. Berlin: Springer (ISBN 978-3-642-23880-2/pbk). Lecture Notes in Computer Science 7002. Lecture Notes in Artificial Intelligence, 450-455 (2011). 2011 Berlin: Springer EN feature selection classification mutual information data mining learning algorithm

doi:10.1007/978-3-642-23881-9_59

Summary: With rapid development of information technology, dimensionality of data in many applications is getting higher and higher. However, many features in the high-dimensional data are redundant. Their presence may pose a great number of challenges to traditional learning algorithms. Thus, it is necessary to develop an effective technique to remove irrelevant features from data. Currently, many endeavors have been attempted in this field. In this paper, we propose a new feature selection method by using conditional mutual information estimated dynamically. Its advantage is that it can exactly represent the correlation between features along with the selection procedure. Our performance evaluations on eight benchmark datasets show that our proposed method achieves comparable performance to other well-established feature selection algorithms in most cases.