Majority Voting System Research Articles

Minority representation in a semi-democratic regime: the Georgian case

This article suggests that in most semi-democracies, the same solution might not be that favourable to minorities. Many semi-democratic countries either restrict party competition or limit parties of ethnic minorities, including: Azerbaijan, Georgia, Kazakhstan, Kirgistan, Cameron, Equatorial Guinea, Tanzania, Gabon, Kenya, Mauritania, and Congo (Brazzaville). This article highlights the impact of the electoral system and the importance of political plurality and electoral district design in such contexts. The article argues that the interests of minorities are best protected if they can elect their representatives in small, ethnically homogeneous electoral districts. Plurality or majority voting systems offer minorities the possibility to run with independent candidates. The case study in this article elections to municipal councils in Georgia in 2006 under a mixed electoral system seem to reflect the hypothesized pattern.

P4-07-14: Circulating Tumor Cells (CTCs) Detection and HER2 Profiling by CellSearch® in Non-Metastatic Breast Cancer: An International Ring Study To Assess Inter-Reader Variability.

Abstract Background: Preliminary results from the Success and Remagus trials showed that CTC detection by CellSearch® has adverse prognostic value in non-metastatic breast cancer. Moreover, several investigators have characterized HER2 expression on CTCs in early breast cancer. Since the majority of CTC-positive women with non-metastatic breast cancer have only 1 CTC detected / blood volume analyzed, we wanted to evaluate the inter-reader variability in this setting. This is a crucial step before moving forward with multi-lab/multi-center prospective testing of the clinical utility of the CellSearch® technology in non-metastatic breast cancer. Methods: Five galleries of CellSearch® images from 3 European and one US institution (a total of 307 images) were mailed to 22 independent readers from 14 European and US academic laboratories and 8 readers from two CellSearch® Veridex laboratories in a blinded fashion. These images came mainly from studies on CTC and/or HER2−positive CTC detection in breast cancer and included healthy women (negative controls), women with metastatic (positive controls) and non-metastatic disease (Pierga et al SABCS 2010, Pierga et al ASCO 2011, Riedthorf et al CCR 2010, Ignatiadis et al PLoS ONE 2011). Each reader reported the images as either CTC-negative or CTC-positive/HER2−negative or CTC-positive/HER2−positive. Kappa statistics were used to assess inter-reader agreement. The 8 Veridex readers were summarized by a majority voting system to derive a gold standard in order to compare each independent reader using discordance rates. Results: Kappa statistics showed moderate to good agreement between independent readers depending on the gallery evaluated (gallery 1 K: 0.55, gallery 2 K:0.57 gallery 3 K:0.64, gallery 4 K:0.72, gallery 5 K:0.85). These differences were attributed to differences in scoring difficulty of each gallery. Discordances in CTC-positive vs CTC-negative events between each reader and the gold standard ranged from 2.3%-31.3% with 7 readers showing discordance rates <5%, 11 readers between 5–10%, 10 readers between 10–14% and only 2 readers showing discordance rates >14% as compared to the gold standard (median discordance rate: 9.3%). Image analysis showed that investigators with high discordance rates compared to the Veridex gold standard were not always taking into account “morphological characteristics” for defining an event as CTC-positive. Discordant results between readers were mainly due to discordance in CTC definition and to a lesser extent in the definition of HER2−positivity. Indeed, discordances in assigning a CTC-positive event as either HER2−negative or HER2−positive ranged from 0.3−13.7% with 19 readers showing discordance rates <5%, 7 readers between 5–10% and 4 readers with discordance rates >10% (median discordance rate: 3.9%). Conclusion: In non-metastatic breast cancer, we observed low discordance between most independent readers and the gold standard for defining a CellSearch® event as CTC with very few readers showing high discordance rates. Concordance can be improved through appropriate training and the use of all the tools provided by the CellSearch® system for image interpretation. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P4-07-14.

Prediction of RNA‐Binding Proteins by Voting Systems

It is important to identify which proteins can interact with RNA for the purpose of protein annotation, since interactions between RNA and proteins influence the structure of the ribosome and play important roles in gene expression. This paper tries to identify proteins that can interact with RNA using voting systems. Firstly through Weka, 34 learning algorithms are chosen for investigation. Then simple majority voting system (SMVS) is used for the prediction of RNA-binding proteins, achieving average ACC (overall prediction accuracy) value of 79.72% and MCC (Matthew's correlation coefficient) value of 59.77% for the independent testing dataset. Then mRMR (minimum redundancy maximum relevance) strategy is used, which is transferred into algorithm selection. In addition, the MCC value of each classifier is assigned to be the weight of the classifier's vote. As a result, best average MCC values are attained when 22 algorithms are selected and integrated through weighted votes, which are 64.70% for the independent testing dataset, and ACC value is 82.04% at this moment.

Prediction of Interaction Between Enzymes and Small Molecules in Metabolic Pathways Through Integrating Multiple Classifiers

Information about interactions between enzymes and small molecules is important for understanding various metabolic bioprocesses. In this article we applied a majority voting system to predict the interactions between enzymes and small molecules in the metabolic pathways, by combining several classifiers including AdaBoost, Bagging and KNN together. The advantage of such a strategy is based on the principle that a predictor based majority voting systems usually provide more reliable results than any single classifier. The prediction accuracies thus obtained on a training dataset and an independent testing dataset were 82.8% and 84.8%, respectively. The prediction accuracy for the networking couples in the independent testing dataset was 75.5%, which is about 4% higher than that reported in a previous study. The web-server for the prediction method presented in this paper is available at http://chemdata.shu.edu.cn/small-enz.

Prediction of interactiveness between small molecules and enzymes by combining gene ontology and compound similarity

Determination of whether a small organic molecule interacts with an enzyme can help to understand the molecular and cellular functions of organisms, and the metabolic pathways. In this research, we present a prediction model, by combining compound similarity and enzyme similarity, to predict the interactiveness between small molecules and enzymes. A dataset consisting of 2859 positive couples of small molecule and enzyme and 286,056 negative couples was employed. Compound similarity is a measurement of how similar two small molecules are, proposed by Hattori et al., J Am Chem Soc 2003, 125, 11853 which can be availed at http://www.genome.jp/ligand-bin/search_compound, while enzyme similarity was obtained by three ways, they are blast method, using gene ontology items and functional domain composition. Then a new distance between a pair of couples was established and nearest neighbor algorithm (NNA) was employed to predict the interactiveness of enzymes and small molecules. A data distribution strategy was adopted to get a better data balance between the positive samples and the negative samples during training the prediction model, by singling out one-fourth couples as testing samples and dividing the rest data into seven training datasets-the rest positive samples were added into each training dataset while only the negative samples were divided. In this way, seven NNAs were built. Finally, simple majority voting system was applied to integrate these seven models to predict the testing dataset, which was demonstrated to have better prediction results than using any single prediction model. As a result, the highest overall prediction accuracy achieved 97.30%.

Multiple classifier integration for the prediction of protein structural classes

Supervised classifiers, such as artificial neural network, partition trees, and support vector machines, are often used for the prediction and analysis of biological data. However, choosing an appropriate classifier is not straightforward because each classifier has its own strengths and weaknesses, and each biological dataset has its own characteristics. By integrating many classifiers together, people can avoid the dilemma of choosing an individual classifier out of many to achieve an optimized classification results (Rahman et al., Multiple Classifier Combination for Character Recognition: Revisiting the Majority Voting System and Its Variation, Springer, Berlin, 2002, 167-178). The classification algorithms come from Weka (Witten and Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, San Francisco, 2005) (a collection of software tools for machine learning algorithms). By integrating many predictors (classifiers) together through simple voting, the correct prediction (classification) rates are 65.21% and 65.63% for a basic training dataset and an independent test set, respectively. These results are better than any single machine learning algorithm collected in Weka when exactly the same data are used. Furthermore, we introduce an integration strategy which takes care of both classifier weightings and classifier redundancy. A feature selection strategy, called minimum redundancy maximum relevance (mRMR), is transferred into algorithm selection to deal with classifier redundancy in this research, and the weightings are based on the performance of each classifier. The best classification results are obtained when 11 algorithms are selected by mRMR method, and integrated together through majority votes with weightings. As a result, the prediction correct rates are 68.56% and 69.29% for the basic training dataset and the independent test dataset, respectively. The web-server is available at http://chemdata.shu.edu.cn/protein_st/.

Explaining Voting System Reform in Canada, 1874 to 1960

This essay explores the efforts to change Canada’s traditional plurality voting system to either majority or proportional voting systems at the municipal, provincial, and federal levels between 1874 and 1960. Specifically, it will provide evidence showing that appeals to populist culture, regional disaffection, and the efforts of individual and party reformers are not sufficient in explaining why, when, and where reforms have occurred. Instead, this essay will demonstrate that serious interest in, and the successful adoption of, majority and proportional voting system reforms in Canada was primarily driven by class factors, specifically the class interests of Canadian farmers and the perceived threat that various labour and socialist parties posed to Canada’s major political parties (and by extension their economic supporters) at different points in Canadian history. By surveying three broad periods of reform efforts, the essay demonstrates that while many different factors—reformer sentiment, party needs, ethnic tensions—may have fueled interest in voting system reforms in Canada, only organized political threats based on class issues motivated any serious or long-standing reform.

Wahl- und Parteiensysteme in Osteuropa: Eine Neubewertung anhand des Konzentrationseffekts

The “laws” postulated by Maurice Duverger and Giovanni Sartori on the connection between electoral and party systems can be tested by looking at the young democracies in post-communist Eastern Europe. Their electoral systems are categorized by type as well as by the predicted strength of the concentration effect. It emerges that in countries with less structured party systems the concentration effect is stronger with proportional representation systems employing national election thresholds than with majority voting systems in single member districts. Such unstable party systems are common among countries in Eastern Europe. While Duverger’s laws are too simplistic to account for the situation in these countries, Sartori’s theory is more applicable due to its emphasis on party structures. However, the latter also falls short because the effects of national election thresholds are neglected. This neglect might be explained by the fact that the thinking of most authors has been influenced by the presence of relatively stable party systems in Western countries.

Representing and reducing error in natural‐resource classification using model combination

Artificial Intelligence (AI) models such as Artificial Neural Networks (ANNs), Decision Trees and Dempster–Shafer's Theory of Evidence have long claimed to be more error‐tolerant than conventional statistical models, but the way error is propagated through these models is unclear. Two sources of error have been identified in this study: sampling error and attribute error. The results show that these errors propagate differently through the three AI models. The Decision Tree was the most affected by error, the Artificial Neural Network was less affected by error, and the Theory of Evidence model was not affected by the errors at all. The study indicates that AI models have very different modes of handling errors. In this case, the machine‐learning models, including ANNs and Decision Trees, are more sensitive to input errors. Dempster–Shafer's Theory of Evidence has demonstrated better potential in dealing with input errors when multisource data sets are involved. The study suggests a strategy of combining AI models to improve classification accuracy. Several combination approaches have been applied, based on a ‘majority voting system’, a simple average, Dempster–Shafer's Theory of Evidence, and fuzzy‐set theory. These approaches all increased classification accuracy to some extent. Two of them also demonstrated good performance in handling input errors. Second‐stage combination approaches which use statistical evaluation of the initial combinations are able to further improve classification results. One of these second‐stage combination approaches increased the overall classification accuracy on forest types to 54% from the original 46.5% of the Decision Tree model, and its visual appearance is also much closer to the ground data. By combining models, it becomes possible to calculate quantitative confidence measurements for the classification results, which can then serve as a better error representation. Final classification products include not only the predicted hard classes for individual cells, but also estimates of the probability and the confidence measurements of the prediction.

Condition monitoring: the case for integrating data from independent sources

A case study is presented in which data from two different sources are combined to create robust diagnostic systems. A diesel engine is used as an exemplar of the class of mechanical machines. Several commonly occurring faults symptomatic of early stages of fault development are induced in the engine. Data in the form of cylinder pressures and vibration are acquired. Orthogonal wavelet transforms, principal component analysis and time domain information are used to extract features from the data. Several artificial neural net classifiers are developed using these data. Statistical models are used to evaluate the diversity within the methodologies used to create the classifiers. The ‘diversity’ metrics are used to propose the most effective majority voting system.

Note on reliability of a system complexity considering entropy

Large‐scale systems have been developed with increasing requirements of high quality and performance. It would be indispensable to define the reliability of more complex systems, considering their complexity. This paper defines a complexity of redundant systems as a logarithmic function of paths, using the concept of an entropy. Further, a reliability function of complexity is given, and the reliabilities of series and parallel systems with complexity are computed. As one of typical redundant systems, a majority voting system is analyzed, and an optimal number of components is derived. Finally, a complexity of network systems is also proposed.

Globalization and Policy Convergence

This article examines the use of the system of mutual recognition, harmonization and qualified majority voting in the European Union (EU) and Australia as a means to the greater understanding of the impact of globalization in relation to policy convergence. It is argued that the Australian adoption of mutual recogntion, based on the EU model, was an example of policy convergence, although the extent of convergence was variable. It is further argued that the convergence was, in part, influenced by globalization. The article is divided into three major sections. The first indicates the author's position in relation to the key concepts of globalization and policy, convergence. The second provides a brief examination of the factors leading to the intensified use of mutual recognition in the EU and its adoption in Australia. The third attempts to assess whether this use of mutual recognition is an example of policy convergence, concluding that, with some reservations, it was. The fourth attempts to assess the role of globalization in’ determining policy, convergence.

After Amsterdam: A Report on the Common Foreign and Security Policy of the European Union

One of the objectives of the 1996-1997 Intergovernmental Conference (IGC) was to improve the functioning of the Common Foreign and Security Policy (CFSP). This was expressly foreseen in the Maastricht Treaty with regard to defence problems. The mandate of the IGC was in fact much broader.1 The results, however, have been quite limited. To give a quick summary, the intergovernmental nature of the CFSP has not been modified. The Treaty of Amsterdam has rationalized the CFSP instruments. The system of qualified majority voting has been simplified, but still allows the use of national vetoes. It has complicated both the decision-making and implementation processes. Finally, a slightly improved long-term perspective on the integration of defence has been exchanged for an equally slightly improved confirmation of NATO's supremacy in the defence realm. The first part of this report looks at the stakes of the debate. The solutions adopted are then examined in the second part This analysis enables us to draw clear conclusions regarding the objectives of the CFSP and the requirements they imply for the Member States.

HIDDEN MARKOV MODELS IN TEXT RECOGNITION

A multi-level multifont character recognition is presented. The system proceeds by first delimiting the context of the characters. As a way of enhancing system performance, typographical information is extracted and used for font identification before actual character recognition is performed. This has the advantage of sure character identification as well as text reproduction in its original form. The font identification is based on decision trees where the characters are automatically arranged differently in confusion classes according to the physical characteristics of fonts. The character recognizers are built around the first and second order hidden Markov models (HMM) as well as Euclidean distance measures. The HMMs use the Viterbi and the Extended Viterbi algorithms to which enhancements were made. Also present is a majority-vote system that polls the other systems for “advice” before deciding on the identity of a character. Among other things, this last system is shown to give better results than each of the other systems applied individually. The system finally uses combinations of stochastic and dictionary verification methods for word recognition and error-correction.

Equitable Voting in the EU?: Options for Change

The disagreement within the European Union (EU) concerning the system of qualified majority voting highlighted the inequalities within the present allocation of votes. With enlargement these inequalities are likely to intensify. We suggest that the EU should examine alternative methods for the allocation of Council votes. Two possible scenarios are allocation according to population size, and second, allocation based upon contributions to the EU budget. We conclude that either of these methods would offer a greater degree of equity, stability and flexibility in the decision-making process of the European Council.

System Reliability: Concepts and Applications

Part 1 Deterministic reliability: Arrhenius' model failure mechanisms screening. Part 2 Statistical reliability: nomenclature operational reliability quantities. Part 3 Statistical failure of components: failure distributions - negative-exponential, normal, lognormal, Weibull and gamma distribution life distribution measurements. Part 4 Reliability models: catastrophic failure model stress-strength model Markov model. Part 5 Non-maintained systems: series systems redundancy parallel systems m-out-of-n systems majority voting systems mixed systems optimization analysis methods. Part 6 Maintained systems: systems with preventive maintenance - scheduled and condition-based systems with corrective maintenance - with and without redundancy, shared-repair facilities, inhomogenous systems maintenance aspects. Part 7 Evaluation methods: causal evaluation - FMEC analysis anti-causal evaluation - fault-tree analysis risk and safety. Part 8 Reliability of computer software: writing reliable software reliability testing failure model. Solutions to problems. Appendices: applied Laplace transforms the Central Limit theorem most commonly used symbols.

Markovian reliability analysis of state-estimator-based instrument fault detection schemes

Concerning their reliability, several state-estimator-based schemes for deterministic and stochastic instrument fault detection and isolation (IFDI) are analysed. Based on the Markovian theory, these schemes and a classical hardware-realized majority voting system are modeled. The resulting models make possible a comparison of the different schemes by calculating their survival probability, their mean life, their availability or other characteristic quantities of reliability. To allow a uniform comparison, an intelligent measuring system (IMS) with three different input variables to be supervised is taken as a basis. For this example, numerical and simulation results show the advantages and disadvantages of each estimator-scheme also compared with the hardware-realized voting system. Additionally, the influence of the quality of estimators will be investigated.

An application of game theory to corporate governance

This paper examines the issue of equity in the distribution of voting power among shareholders of a company. The prevailing one-share-one-vote simple majority voting system is seen to give to certain shareholders voting power out of proportion to their shareholdings, and ways of bringing about a more equitable voting power distribution by changing the threshold for winning or departing from the one-share-one-vote principle are studied. Several effective solutions are found and their practical implications are discussed.

The West German electoral system: The case for reform, 1949–87

Since the 1960s the question of electoral reform (specifically the introduction of a majority voting system) has not been a live issue in West Germany. The wide acceptance of proportional representation has much to do with changes in political culture ‐ a greater tolerance of conflict and less concern with stability. Foreign observers often treat the West German system as a model. However, the two‐vote system, although seen as maximising electoral choice, is seriously defective: it is confusing, encourages manipulation, and gives voters the false impression of having a greater degree of choice than is actually the case.

A Neo‐Ricardian Trade Model with Overlapping Generations*

This paper develops and examines a discrete‐time two‐by‐two‐by‐two barter trading world which incorporates populations. composed of overlapping, life‐cycle‐maximizing generations; each provides an economically distinguishable factor of production. It is shown that free trade without compensation can be Pareto inferior to autarky for one country. Conditions for uncompensated free trade to take place under a majority voting system are derived. The model permits intergenerational borrowing and lending to take place domestically but not internationally.