We investigate the problem of using past performance information to select an algorithm for a given classiication problem. We present three ranking methods for that purpose: average ranks, success rate ratios and signiicant wins. We also analyze the problem of evaluating and comparing these methods. The evaluation technique used is based on a leave-one-out procedure. On each iteration, the meth...

There are numerous methods to rank sports teams. Each of these methods incorporate different components to determine a ranking system. For example, the Rating Percentage Index (RPI) method considers the weighted average of a team’s winning percentage and its opponents winning percentage [16]. The Elo method assigns each team a rating and when two teams play each other, the rankings change depen...

An automatic term extraction system consists of a term candidate extraction subsystem, a ranking subsystem and a selection subsystem. In this paper, we experimentally evaluate two ranking methods and two selection methods. As for ranking, a dichotomy of unithood and termhood is a key notion. We evaluate these two notions experimentally by comparing Imp based ranking method that is based directl...

Centroid and spread are commonly used approaches in ranking fuzzy numbers. Some experts rank fuzzy numbers using centroid or spread alone while others tend to integrate them together. Although a lot of methods for ranking fuzzy numbers that are related to both approaches have been presented, there are still limitations whereby the ranking obtained is inconsistent with human intuition. This pape...

Recursive Feature Elimination RFE combined with feature-ranking is an effective technique for eliminating irrelevant features. In this paper, an ensemble of MLP base classifiers with feature-ranking based on the magnitude of MLP weights is proposed. This approach is compared experimentally with other popular feature-ranking methods, and with a Support Vector Classifier SVC. Experimental results...

Data Envelopment Analysis (DEA) technique now widely use for efficiency evaluation of a set of Decision Making Units (DMUs). As regards of the necessity for ranking efficient units different DEA models presented each of which has advantages and rank efficient units from special aspects. Note that all the existing ranking models have disadvantages, as well and there is not a model in which all t...

The full ranking or complete ranking of decision making units is one of the main ‎issues in data envelopment analysis. A full ranking is a ranking that considers all ‎efficient and inefficient units simultaneously and finds a ranking for them. Almost all ‎of the developed ranking methods consider only the efficient units. On the other hand, ‎ranking inefficient units by traditional data envelop...

Screening large libraries of chemical compounds against a biological target, typically a receptor or an enzyme, is a crucial step in the process of drug discovery. Virtual screening (VS) can be seen as a ranking problem which prefers as many actives as possible at the top of the ranking. As a standard, current Quantitative Structure-Activity Relationship (QSAR) models apply regression methods t...

Within data envelopment analysis is a subgroup of papers in which many researchers have sought to improve the differential capabilities of DEA and to fully rank the decision making units (DMU). However, whilst each technique is useful in a specialist area, no one methodology can be prescribed as the complete solution to the question of ranking. Different results which are reached in applying th...

This paper describes our participation in the MWE 2008 evaluation campaign focused on ranking MWE candidates. Our ranking system employed 55 association measures combined by standard statistical-classification methods modified to provide scores for ranking. Our results were crossvalidated and compared by Mean Average Precision. In most of the experiments we observed significant performance impr...