In class-imbalance learning, Synthetic Minority Oversampling Technique (SMOTE) is a widely used technique to tackle problems from the data level, whereas SMOTE blindly selects neighboring minority class points when performing an interpolation among them and inevitably brings collinearity between generated new original ones. To combat these problems, we propose in this study adaptive-weighting m...

The canonical correlations between subsets of OLS estimators are identified with design linkage parameters between their regressors. Known collinearity indices are extended to encompass angles between each regressor vector and remaining vectors. One such angle quantifies the collinearity of regressors with the intercept, of concern in the corruption of all estimates due to ill-conditioning. Mat...

A major bottleneck to introgressive hybridization is the lack of genome collinearity between the donor (alien) genome and the recipient crop genome. Structural differences between the homeologs may create unbalanced segregation of chromosomes or cause linkage drag. To assess large-scale collinearity between potato and two of its wild relatives (Solanum commersonii and Solanum chacoense), we use...

In this paper, a new approach for the reconstruction of a 3D plane by solving the geometry of two perspective projections of the plane is presented. It is assumed that the position and the orientation of one camera are known with respect to the other one. The correspondence between the object space and image planes is established using the collinearity equation. The projection of the original p...

Novel 3D measures of compressibility and isotropy (coaxiality collinearity) are proposed to test constitutive laws rotating drum flows using the DEM with μ(I) rheology is tested. The coaxiality measure produces a lower occurrence frequency than equivalent 2D simulation for nearly all angular separations, while collinearity shows minimal deviation. high degree facilitated by across gravity-drive...

Abstract In many fields of applications, linear regression is the most widely used statistical method to analyze effect a set explanatory variables on response variable interest. Classical least squares focuses conditional mean response, while quantile extends view quantiles. Quantile very convenient, whereas classical parametric assumptions do not hold and/or when relevant information lies in ...