Estimating statistical significance of detected differences between two groups of medical scans is a challenging problem due to the high dimensionality of the data and the relatively small number of training examples. In this paper, we demonstrate a non-parametric technique for estimation of statistical significance in the context of discriminative analysis (i.e., training a classifier function...

the field study was conducted in one district of educational-experimental forest at tehran university (kheirood-kenar forest) in the north of iran. eighty-five soil profiles were dug in the site of study and several chemical and physical soil properties were considered. these factors included: soil ph, soil texture, bulk density, organic carbon, total nitrogen, extractable phosphorus and depth ...

Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or ‘sources’ from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption of mutual independence between the signals. The weakness of the assumptions makes it a powerful approach but...

Roughly Balanced Bagging is based on under-sampling and classifies imbalanced data much better than other ensembles. In this paper, we experimentally study its properties that may influence its good performance. Results of experiments show that it can be constructed with a small number of component classifiers, which are quite accurate, however, of low diversity. Moreover, its good performance ...

The present paper addresses the problem of the classification of hyperspectral images with multiple imbalanced classes and very high dimensionality. Class imbalance is handled by resampling the data set, whereas PCA is applied to reduce the number of spectral bands. This is a preliminary study that pursues to investigate the benefits of using together these two techniques, and also to evaluate ...

The last several years have seen a growth in the number of publications in economics that use principal component analysis (PCA), especially in the area of welfare studies. This paper gives an introduction into the principal component analysis and describes how the discrete data can be incorporated into it. The effects of discreteness of the observed variables on the PCA are overviewed. The con...