We introduce an algorithm, called Robust Fuzzy Clustering for Multiple Instance Regression (RFC-MIR), that can learn multiple linear models simultaneously. First, RFC-MIR uses constrained fuzzy memberships to obtain an initial partition where instances can belong to multiple models with various degrees...
I have used the Essential Regression software in Excel while doing Multiple Regression so far however, the software is only supported till Windows 8 versions and not above that. I am currently using Windows 10 and was wondering how to do an Auto Regression (AutoFit) for given source of data....
The multiple instance problem arises in tasks where the training examples are ambiguous: a single example object may have many alternative feature vectors (instances) that describe it, and yet only one of those feature vectors may be responsible for the observed classification of the object. This ...
In this paper, we apply MCHP to Robust Ordinal Regression (ROR) being a family of MCDA methods that takes into account all sets of parameters of an assumed preference model, which are compatible with preference information elicited by a Decision Maker (DM). As a result of ROR, one gets ...
For instance, we have utilized scCube to generate a series of simulated spot-based SRT data with different resolutions but the same other spatial variability (such as the number, proportion and spatial distribution of cell types) and benchmarked nine widely used spot deconvolution methods. ...
(Fig.4; measured by average out-of-sample McFadden’sR2for logistic regression;Methods). While the increased performance of local ancestry in some regions compared with regular GWAS can be explained by tagging of SNPs outside the region, the increased performance of HTRX over GWAS quantifies ...
Putting Dominance-based Rough Set Approach and robust ordinal regression together We propose to apply the Dominance-based Rough Set Approach (DRSA) on the results of multiple criteria decision aiding (MCDA) methods, in order to explain t... S Greco,Roman Stowinski,P Zielniewicz - 《Decision...
The issue of ambiguity has been resolved by Greco et al. (2008) using the approach called Robust Ordinal Regression (ROR) (Greco et al.2010d). In ROR, the preference data are the same as in UTA, however, the ordinal regression finds the whole set of compatible instances of the value ...
Multiple linear regression OG: Orthogneiss PC: Principal component PCA: Principal component analysis SG: Sillimanite and garnet-bearing biotite gneiss D : Bulk density, g/cm3 FD: Fracture density, m−1 GR: Gamma ray, API K : Potassium, ppm N : Neutron porosity, v/v P10:...
Such a structural nonparametric regression can effectively avoid the curse of the model parameter heterogeneity (see, for instance [3], [4], [5], [6]). On the contrary, as a solution to control the influential observations on over-fitting [7], [8], the robust regression has gained a ...