Information criteria performance for feature selection

摘要：

This paper shows the information criteria (IC) performances in feature selection framework. Feature selection aims to select a representative subset among a wide set of features. We apply this approach to classify an hand segmented image. The performance is tested using various feature selection schemes (SFS, SBS, SFFS and SBFS) to select the candidate subsets. The accuracy of the approach is based on a good quality of the joint probability density approximation of the combined features. They are obtained using histogram optimized thanks to the adaptive arithmetic coding principles. Our approach is tested on different reference data. The subsets quality is evaluated using correct classification rate computed on multiple classifiers. Results show stability and convergence properties of this tool and its ability to select representative subsets (in the sense that the subset of feature is a good characterization of the classes in which the data belong). Information Criteria could be used for feature selection as a good alternative to other criteria.

关键词： component information criteria feature selection histogram estimation and selection feature scheme search

作者: Mohamed Abadi Olivier Alata Christian Olivier Majdi Khoudeir Enguerran Grandchamp

作者单位: University of Poitiers XLIM-SIC department UMR CNRS 6172 Chasseneuil-Futuroscope, France University of Antilles and Guyana LAMIA Pointe-a-Pitre, Guadeloupe, France

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 933-937

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）