This paper describes an R package, rpartOrdinal, that implements alternative splitting functions for fitting a classification tree when interest lies in predicting an ordinal response. This includes the generalized Gini impurity function, which was introduced as a method for predicting an ordinal response by including costs of misclassification into the impurity function, as well as an alternat...

Abstraci—What is learned during mastery of a serial task: associations between adjacent and remote items, associations between an item and its ordinal position, or both? A dear answer to this question is lacking in the literature on human serial memory because it is difficult to control for a "naive" subject's linguistic competence and extensive experience with serial tasks. In this article, we...

Growth data and longitudinal data in general are often of an ordinal nature. For example, developmental stages may be classified into ordinal categories and behavioral variables repeatedly measured by discrete ordinal scales. Consider the data set presented in Table 15.1. This table contains information on marijuana use taken from five annual waves (1976–80) of the National Youth Survey (Elliot...

Given a collection of individual ordinal probabilities on a …nite state space, we discuss an ordinal condition that is necessary and su¢ cient for an ordinal probability to be represented as a weighted average of probability representations of the individual probabilities. We also give necessary and su¢ cient conditions for when such an ordinal probability can be represented as an unweighted av...

Ordinal optimization has emerged as an efficient technique for simulation and optimization, converging exponentially in many cases. In this paper, we present a new computing budget allocation approach that further enhances the efficiency of ordinal optimization. Our approach intelligently determines the best allocation of simulation trials or samples necessary to maximize the probability of ide...

Ordinal embedding refers to the following problem: all we know about an unknown set of points x1, . . . , xn ∈ R are ordinal constraints of the form ‖xi−xj‖ < ‖xk−xl‖; the task is to construct a realization y1, . . . , yn ∈ R that preserves these ordinal constraints. It has been conjectured since the 1960ies that upon knowledge of all ordinal constraints a large but finite set of points can be ...

In order to develop fast and robust methods for extracting qualitative information from non-linear time series, Bandt and Pompe have proposed to consider time series from the pure ordinal viewpoint. On the base of counting ordinal patterns, which describe the up-and-down in a time series, they have introduced the concept of permutation entropy for quantifying the complexity of a system behind a...

We consider ordinal approximation algorithms for a broad class of utility maximization problems for multi-agent systems. In these problems, agents have utilities for connecting to each other, and the goal is to compute a maximum-utility solution subject to a set of constraints. We represent these as a class of graph optimization problems, including matching, spanning tree problems, TSP, maximum...