Title :
Enhancing Classification Accuracy with the Help of Feature Maximization Metric
Author :
Lamirel, Jean-Charles
Author_Institution :
Synalp Team, LORIA, Vandœuvre-lès-Nancy, France
Abstract :
This paper deals with a new feature selection and feature contrasting approach for enhancing classification of both numerical and textual data. The method is experienced on different types of reference datasets. The paper illustrates that the proposed approach provides a very significant performance increase in all the studied cases clearly figuring out its generic character.
Keywords :
feature selection; optimisation; pattern classification; classification accuracy enhancement; feature contrasting approach; feature maximization metric; feature selection; numerical data; reference datasets; textual data; Accuracy; Classification algorithms; Context; Measurement; Niobium; Principal component analysis; Standards; classification; feature maximization; feature selection; numerical data; text;
Conference_Titel :
Tools with Artificial Intelligence (ICTAI), 2013 IEEE 25th International Conference on
Conference_Location :
Herndon, VA
Print_ISBN :
978-1-4799-2971-9
DOI :
10.1109/ICTAI.2013.90