Paper Title
Booster in High Dimensional Data Classification

Classification issues in high dimensional knowledge with tiny variety of observations have become additional common particularly in microarray knowledge. The increasing quantity of text info on the net sites affects the agglomeration analysis[1]. The text agglomeration may be a favorable analysis technique used for partitioning a colossal quantity of knowledge into clusters. Hence, the most important downside that affects the text agglomeration technique is that the presence uninformative and distributed options in text documents. A broad category of boosting algorithms is understood as acting coordinate-wise gradient descent to attenuate some potential perform of the margins of an information set[1]. This paper proposes a brand new analysis live Q-statistic that comes with the soundness of the chosen feature set additionally to the prediction accuracy. Then we have a tendency to propose the Booster of associate degree FS algorithmic rule that enhances the worth of the Q-statistic of the algorithmic rule applied. Keywords - high dimensional data classification; feature selection; stability; Q-statistic; Booster;