A GLFES and DFT Technique for Feature Selection in High-Dimensional Imbalanced dataset

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 toefficiency 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.