This paper is a continuation of the study of surprise as a base for constructing qualitative calculi for representing and reasoning about uncertain knowledge. Here, we further elaborate on κ, a qualitative ranking function which we developed in (Ibrahim, Tawfik, and Ngom 2009b) and which constructs qualitative ranks for events by obtaining the order of magnitude abstraction of the degree of sur...

Given that wide variety of available classiication algorithms exists, the selection of the right algorithm to use on a new problem is an important issue. In this paper we present zooming, that analyzes a given dataset and selects relevant (similar) datasets used in the past. This process is based on the \distance" calculated on the basis of several dataset characteristics. The accuracy and time...

We introduce the problem of PAC rank elicitation, which consists of sorting a given set of options based on adaptive sampling of stochastic pairwise preferences. More specifically, we assume the existence of a ranking procedure, such as Copeland’s method, that determines an underlying target order of the options. The goal is to predict a ranking that is sufficiently close to this target order w...

Any practical process of risk ranking must group hazards into a manageable number of categories. Defining such categories requires value choices that can have important implications for the rankings that result. Most risk-management organizations will find it useful to begin defining categories in terms of environmental loadings or initiating events. However, the resulting categories typically ...

This study tested some predictions of the local-resource-competition hypothesis and localresource-enhancement hypothesis of sex-biased maternal investment in a population of pigtail macaques (Macaca nemestrina leonina). Offspring sex ratios in highand lowranking matrilines were biased in opposite directions as predicted. Sex ratio in the middleranking matriline was female biased similar to the ...

The present paper examines how the aggregation and feature randomization principles underlying the algorithm Random Forest (Breiman (2001)) can be adapted to bipartite ranking. The approach taken here is based on nonparametric scoring and ROC curve optimization in the sense of the AUC criterion. In this problem, aggregation is used to increase the performance of scoring rules produced by rankin...

The paper first explains how the ranking-theoretic belief change or conditionalization rules entail all of the standard AGM belief revision and contraction axioms. Those axioms have met a lot of objections and counter-examples, which thus extend to ranking theory as well. The paper argues for a paradigmatic set of cases that the counter-examples can be well accounted for with various pragmatic ...

The ever increasing size of datasets used for data mining and machine learning applications has placed a renewed emphasis on algorithm performance and processing strategies. This paper addresses algorithms for ranking variables in a dataset, as well as for ranking values of a specific variable. We propose two new techniques, called Max Gain (MG) and Sum Max Gain Ratio (SMGR), which are well-cor...

User-generated content has dominated the web’s recent growth and today the so-called real-time web provides us with unprecedented access to the real-time opinions, views, and ratings of millions of users. For example, Twitter’s 200m+ users are generating in the region of 1000+ tweets per second. In this work, we propose that this data can be harnessed as a useful source of recommendation knowle...