Improving Prediction Accuracy Results by Using Q-Statistic Algorithm in High Dimensional Data

Abstract

Classification problems in high dimensional information with little sort of observations became furthercommon significantly in microarray information. The increasing amount of text data on internet sites affects the agglomerationanalysis. The text agglomeration could also be a positive analysis technique used for partitioning a huge amount of datainto clusters. Hence, the most necessary draw back that affects the text agglomeration technique is that the presenceuninformative and distributed choices in text documents. A broad class of boosting algorithms is known as actingcoordinate-wise gradient descent to attenuate some potential performs of the margins of a data set. This paperproposes a novel analysis live Q-statistic that comes with the soundness of the chosen feature set to boot to theprediction accuracy. Then we've a bent to propose the Booster of associate degree FS algorithm that enhances theworth of the Q-statistic of the algorithm applied.