Published Paper Details:

Classification problems in high dimensional data with small number of observations are becoming more common especially in microarray data. The increasing amount of text information on the Internet web pages affects the clustering analysis [1]. The text clustering is a favorable analysis technique used for partitioning a massive amount of information into clusters. Hence, the major problem that affects the text clustering technique is the presence uninformative and sparse features in text documents. A broad class of boosting algorithms can be interpreted as performing coordinate-wise gradient descent to minimize some potential function of the margins of a data set [1]. This paper proposes a new evaluation measure Q-statistic that incorporates the stability of the selected feature subset in addition to the prediction accuracy. Then we propose the Booster of an FS algorithm that boosts the value of the Q statistic of the algorithm applied.