This paper is concerned with rank aggregation, which aims to combine multiple input rankings to get a better ranking. A popular approach to rank aggregation is based on probabilistic models on permutations, e.g., the Luce model and the Mallows model. However, these models have their limitations in either poor expressiveness or high computational complexity. To avoid these limitations, in this p...

In order to clarify the variable selection of Lasso, Lasso is compared with two other methods AIC and stagewise forward. First, that AIC, it discovered has a wider application range than AIC. The data simulation shows under orthonormal design consistent can be solved by using algorithm stepwise selection, removed variables appear again nonorthonormal design, isn’t We continue compare between fo...

We consider the problem of ordinal classification, in which a value set of the decision attribute (output, dependent variable) is finite and ordered. This problem shares some characteristics of multi-class classification and regression, however, in contrast to the former, the order between class labels cannot be neglected, and, in the contrast to the latter, the scale of the decision attribute ...

The cytokinesis-block micronucleus (CBMN) assay can be used to quantify micronucleus (MN) formation, the outcome measured being MN frequency. MN frequency has been shown to be both an accurate measure of chromosomal instability/DNA damage and a risk factor for cancer. Similarly, the Agilent 4×44k human oligonucleotide microarray can be used to quantify gene expression changes. Despite the exist...

We congratulate the authors (hereafter BH) for an interesting take on the boosting technology, and for developing a modular computational environment in R for exploring their models. Their use of low-degree-of-freedom smoothing splines as a base learner provides an interesting approach to adaptive additive modeling. The notion of “Twin Boosting” is interesting as well; besides the adaptive lass...

Boosting has been a very successful technique for solving the two-class classification problem. In going from two-class to multi-class classification, most algorithms have been restricted to reducing the multi-class classification problem to multiple two-class problems. In this paper, we develop a new algorithm that directly extends the AdaBoost algorithm to the multi-class case without reducin...

We congratulate the authors (hereafter BH) for an interesting take on the boosting technology, and for developing a modular computational environment in R for exploring their models. Their use of low-degree-offreedom smoothing splines as a base learner provides an interesting approach to adaptive additive modeling. The notion of “Twin Boosting” is interesting as well; besides the adaptive lasso...

Researchers have recently shown that penalized models perform well when applied to high-throughput genomic data. Previous researchers introduced the generalized monotone incremental forward stagewise (GMIFS) method for fitting overparameterized logistic regression models. The GMIFS method was subsequently extended by others for fitting several different logit link ordinal response models to hig...