In this paper we deal with graph classification. We propose a new algorithm for performing sparse logistic regression on graphs. Sparsity is required for the reason of interpretability, which is often necessary in domains such as bioinformatics or chemoinformatics. Our method is comparable in accuracy with other methods of graph classification and produces probabilistic output in addition.

Binary logistic regression with a sparsity constraint on the solution plays a vital role in many high dimensional machine learning applications. In some cases, the features can be grouped together, so that entire subsets of features can be selected or zeroed out. In many applications, however, this can be very restrictive. In this paper, we are interested in a less restrictive form of structure...

Binomial data with unknown sizes often appear in biological and medical sciences. The previous methods either use the Poisson approximation or the quasi-likelihood approach. A full likelihood approach is proposed by treating unknown sizes as latent variables. This approach simplifies analysis as maximum likelihood estimation can be applied. It also facilitates us to gain a lot more insights int...

In this paper we present a simple hierarchical Bayesian treatment of the sparse kernel logistic regression (KLR) model based MacKay’s evidence approximation. The model is re-parameterised such that an isotropic Gaussian prior over parameters in the kernel induced feature space is replaced by an isotropic Gaussian prior over the transformed parameters, facilitating a Bayesian analysis using stan...

In this paper, we develop a simulation-based framework for regularized logistic regression, exploiting two novel results for scale mixtures of normals. By carefully choosing a hierarchical model for the likelihood by one type of mixture, and implementing regularization with another, we obtain new MCMC schemes with varying efficiency depending on the data type (binary v. binomial, say) and the d...

This review introduces logistic regression, which is a method for modelling the dependence of a binary response variable on one or more explanatory variables. Continuous and categorical explanatory variables are considered.

Menteshashvili O, et al. Pulmonary tuberculosis in prisons of the ex-USSR state Georgia: results of a nationwide prevalence survey among sentenced inmates. Antimicrobial prescribing patterns for respiratory diseases including tuberculosis in Russia: a possible role in drug resistance? Jpyridinium chloride is useful for isolation of Mycobacterium tuberculosis from sputa subjected to long-term st...

Subjects were engineering majors recruited from a freshman-level engineering class from 2007 through 2010. Data were obtained for 256 students. The outcome variable of interest was retention group: Those who were still active in our engineering program after two years of study were classified as persisters. Those who were no longer in our engineering program were classified as having left in go...

We consider logistic regression with arbitrary outliers in the covariate matrix. We propose a new robust logistic regression algorithm, called RoLR, that estimates the parameter through a simple linear programming procedure. We prove that RoLR is robust to a constant fraction of adversarial outliers. To the best of our knowledge, this is the first result on estimating logistic regression model ...