Paper Title
Booster in High Dimensional Data Classification
Abstract
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;