期刊名称：Indian Journal of Computer Science and Engineering
印刷版ISSN：2231-3850
电子版ISSN：0976-5166
出版年度：2012
卷号：3
期号：2
页码：336-343
出版社：Engg Journals Publications
摘要：Feature selection has been an active research area in pattern recognition, statistics ,and data mining communities Feature selection, is a preprocessing step to machine learning, is effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving result comprehensibility. However, the recent increase of dimensionality of data poses severe challenge to many existing feature selection methods with respect to efficiency and effectiveness. Feature Selection in High-Dimensional Imbalanced Dataset (where one class outnumbers the others) plays a significant task in the field of Data mining. Discarding data and adding data sometimes may affect the performance. This paper proposes a new approach GLFES (Granularity learning Fuzzy Evolutionary Sampling) and DFT (Defuzzification Technique) for Feature Selection. It is evaluated on micro array datasets.
