Feature selection has been applied in several areas of science and engineering for a long time. This kind of pre-processing is almost mandatory in problems with huge amounts of features which requires a very high computational cost and also may be handicapped very frequently with more than two classes and lot of instances. The general taxonomy clearly divides the approaches into two groups such as filters and wrappers. This paper introduces a methodology to refine the feature subset with an additional feature selection approach. It reviews the possibilities and deepens into a new class of algorithms based on a refinement of an initial search with another method. We apply sequentially an approximate procedure and an exact procedure. The research is supported by empirical results and some guidelines are drawn as conclusions of this paper.
|Title of host publication||Intelligent Data Engineering and Automated Learning – IDEAL 2017 - 18th International Conference, Proceedings|
|Editors||Hujun Yin, Minling Zhang, Yimin Wen, Guoyong Cai, Tianlong Gu, Antonio J. Tallon-Ballesteros, Junping Du, Yang Gao, Songcan Chen|
|Number of pages||7|
|Publication status||Published - 2017|
|Event||18th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2017 - Guilin, China|
Duration: 2017 Oct 30 → 2017 Nov 1
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||18th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2017|
|Period||17/10/30 → 17/11/1|
Bibliographical notePublisher Copyright:
© Springer International Publishing AG 2017.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)