Many pattern classification algorithms such as Support Vector Machines (SVMs), MultiLayer Perceptrons (MLPs), and K-Nearest Neighbors (KNNs) require data to consist of purely numerical variables. However many real world data consist of both categorical and numerical variables. In this paper we suggest an effective method of converting the mixed data of categorical and numerical variables into d...

In this paper, we consider the supervised learning task which consists in predicting the normalized rank of a numerical variable. We introduce a novel probabilistic approach to estimate the posterior distribution of the target rank conditionally to the predictors. We turn this learning task into a model selection problem. For that, we define a 2D partitioning family obtained by discretizing num...

Based on mixture models, we present a Bayesian method (called BClass) to classify biological entities (e.g. genes) when variables of quite heterogeneous nature are analyzed. Various statistical distributions are used to model the continuous/categorical data commonly produced by genetic experiments and large-scale genomic projects. We calculate the posterior probability of each entry to belong t...

Distributions on classifications are met wherever we work with categorical variables. A vast investigation has been done in developing methods for statistical analysis of nominal variables (e.g. variables with simple classification), partially are solved also problems for ordered classifications and classifications with assigned numbers. In this paper we propose a general model which enables us...

Given a population described by p explanatory and one dependent categorical variables, we assume that the dependent variable defines a partition of the population into g groups. Discriminant Analysis studies the relation between the p explanatory variables and the dependent variable finding the subset of variables that has the most predictive power. Generally, in categorical discriminant analys...

Abstract In low-resource domains, it is challenging to achieve good performance using existing machine learning methods due a lack of training data and mixed types (numeric categorical). particular, categorical variables with high cardinality pose challenge tasks such as classification regression because requires sufficiently many points for the possible values each variable. Since interpolatio...

Prior studies have demonstrated the safety of same day discharge in appropriately selected patients undergoing CIED implantation though a limitation is requirement for prolonged post-operative observation. There limited data on whether expedited pathways, which can reduce resource utilization, offer comparably safe outcomes. To evaluate outcomes an (SDD) protocol across hospitals. All pacemaker...

Latent variable models exist with continuous, categorical, or both types of latent variables. The role of latent variables is to account for systematic patterns in the observed responses. This article has two goals: (a) to establish whether, based on observed responses, it can be decided that an underlying latent variable is continuous or categorical, and (b) to quantify the effect of sample si...

Recursive path analysis is a useful tool for inference on a sequence of three or more response variables in which the causal effects of variables, if any, are in one direction. The primary objective in such analysis is to decompose the total effect of each variable into its direct and indirect components. Methods for recursive analysis of a chain of continuous variables are well developed but t...