We consider the problem of classification in noisy, high-dimensional, and class-imbalanced protein datasets. In order to design a complete classification system, we use a three-stage machine learning framework consisting of a feature selection stage, a method addressing noise and class-imbalance, and a method for combining biologically related tasks through a prior-knowledge based clustering. I...

Traditional Lot Quality Assurance Sampling (LQAS) methods require random sampling to guarantee valid results. However, cluster sampling has been proposed to reduce the number of random starting points. This study uses simulations to examine the classification error of two such designs, a 67x3 (67 clusters of 3 observations) and a 33x6 (33 clusters of 6 observations) sampling scheme to assess th...

Gupta and Shabbir (2008) have suggested an alternative form of ratio-type estimator for estimating the population mean. In this paper, we introduced new estimators by mixing two, stratified and cluster sampling method. Then we improved these estimators by using auxiliary variables and introducing new estimators. For sampling in infinite populations with a high geographic dispersion, the populat...

introduction the human immunodeficiency virus (hiv) is a retrovirus that infects cells of the immune system, destroying or impairing their function. as the infection progresses, the immune system becomes weaker, and the person becomes more susceptible to infections. the most advanced stage of hiv infection is acquired immunodeficiency syndrome (aids). the present study aimed to evaluate the eff...

OBJECTIVE To construct a design for probabilistic sampling of reporting physicians in sentinel networks. METHODS We performed a multi-stage sample selection study. Data on primary care physicians and their patients were obtained from the Madrid Health Institute for 2005. The geographical unit of reference was the basic health area. A factorial analysis was performed on the basis of demographi...