Search results

This article develops an alternative theoretical approach to the Supreme Court’s controversial electoral redistricting decisions in Shaw v. Reno (1993) and its progeny. Instead of relying on the traditional equal protection interpretation, this paper argues that controversies over electoral redistricting are at base disputes among competing visions of democracy. In the Court’s recent redistricting cases, the majority and the dissent adopted fundamentally different visions of democracy – Individualist Democracy and Democracy as Power. In addition to elaborating these rival understandings of democracy, this article develops the concept of Symbolic Democracy to explain a central paradox in the Court majority’s decision: its simultaneous denial and recognition of the relevance of racial groups in representation.

Multiple classifier systems have been used widely in computing, communications, and informatics. Combining multiple classifier systems (MCS) has been shown to outperform a single classifier system. It has been demonstrated that improvement in ensemble performance depends on either the diversity among or the performance of individual systems. A variety of diversity measures and ensemble methods have been proposed and studied. However, it remains a challenging problem to estimate the ensemble performance in terms of the performance of and the diversity among individual systems. The purpose of this paper is to study the general problem of estimating ensemble performance for various combination methods using the concept of a performance distribution pattern (PDP).

In particular, the paper establishes upper and lower bounds for majority voting ensemble performance with disagreement diversity measure Dis, weighted majority voting performance in terms of weighted average performance and weighted disagreement diversity, and plurality voting ensemble performance with entropy diversity measure D.

Bounds for these three cases are shown to be tight using the PDP for the input set.

As a consequence of the authors' previous results on diversity equivalence, the results of majority voting ensemble performance can be extended to several other diversity measures. Moreover, the paper showed in the case of majority voting ensemble performance that when the average of individual systems performance P is big enough, the ensemble performance P^m resulting from a maximum (information‐theoretic) entropy PDP is an increasing function with respect to the disagreement diversity Dis. Eight experiments using data sets from various application domains are conducted to demonstrate the complexity, richness, and diverseness of the problem in estimating the ensemble performance.

A crucial decision in financial services is how to classify credit or loan applicants into good and bad applicants. The purpose of this paper is to propose a four-stage hybrid data mining approach to support the decision-making process.

The approach is inspired by the bagging ensemble learning method and proposes a new voting method, namely two-level majority voting in the last stage. First some training subsets are generated. Then some different base classifiers are tuned and afterward some ensemble methods are applied to strengthen tuned classifiers. Finally, two-level majority voting schemes help the approach to achieve more accuracy.

A comparison of results shows the proposed model outperforms powerful single classifiers such as multilayer perceptron (MLP), support vector machine, logistic regression (LR). In addition, it is more accurate than ensemble learning methods such as bagging-LR or rotation forest (RF)-MLP. The model outperforms single classifiers in terms of type I and II errors; it is close to some ensemble approaches such as bagging-LR and RF-MLP but fails to outperform them in terms of type I and II errors. Moreover, majority voting in the final stage provides more reliable results.

The study concludes the approach would be beneficial for banks, credit card companies and other credit provider organisations.

A novel four stages hybrid approach inspired by bagging ensemble method proposed. Moreover the two-level majority voting in two different schemes in the last stage provides more accuracy. An integrated evaluation criterion for classification errors provides an enhanced insight for error comparisons.

In 2010, the National Mediation Board (NMB) decided to base Railway Labor Act representation election outcomes on a simple majority of those voting, rather than on the majority of all eligible voters, as had been required earlier. This was widely expected to make it easier for unions to win rights to recognition in the railway and airline industries. We demonstrate that investors expected that this change would favor unions, just as they earlier had expected rule changes that made voting easier (in 2002 and 2007) to be favorable to unions, affecting stock prices of railway and airline corporations. After the 2010 change in election procedure, between 77% and 91% of all eligible employees returned ballots in NMB elections, demonstrating that a significant portion of nonvoters were not opposed to union representation, but simply were unwilling or were unable to vote. We conclude that the current voting process is fairer than the old one. However, it has not resulted in a tide of union success in these representation elections. Apparently scholars, the parties themselves, and investors all over-estimated the practical consequences of changing NMB representation election procedures.

Based on a formalization of the 1981 constitutional negotiation in Canada, this article analyses the impact of procedural constraints on collective decisions. Four procedural constraints are considered: voting procedures, voting rules, decision rules, and the order of presentation of options to the vote. Sincere voting (voting according to a voter's preference scale), complete information, and free communication are assumed in the first part of the analysis. The assumption of sincere voting is relaxed in the second part where strategic voting is considered The analysis shows that (1) a collective decision is determined by some interaction of voters' preferences, procedural constraints, and voting strategies, and (2) procedural constraints can be ordered in terms of their relative impact on the collective decision (in decreasing order: decision rules, voting rules, order of presentation, voting procedures). In the conclusion, a general model of the determination of collective decisions is presented.

Blockholders can behave opportunistically because small shareholder voting suffers from coordination problems. In this chapter, we investigate the features of small shareholder voting using a theoretical framework. Specifically, we investigate when defeating a blockholder’s resolution is optional for shareholders. Regulatory initiatives that facilitate communication between small shareholders or focus on institutional investors and corporate governance tools that alter or add the threshold in the voting game also contribute to solving the coordination problem. These corporate governance initiatives can increase the relevance of AGMs in Europe.

This book is a policy proposal aimed at the democratic left. It is concerned with gradual but radical reform of the socio‐economic system. An integrated policy of industrial and economic democracy, which centres around the establishment of a new sector of employee‐controlled enterprises, is presented. The proposal would retain the mix‐ed economy, but transform it into a much better “mixture”, with increased employee‐power in all sectors. While there is much of enduring value in our liberal western way of life, gross inequalities of wealth and power persist in our society.

Hundreds of thousands of deaths each year in the world are caused by breast cancer (BC). An early-stage diagnosis of this disease can positively reduce the morbidity and mortality rate by helping to select the most appropriate treatment options, especially by using histological BC images for the diagnosis.

The present study proposes and evaluates a novel approach which consists of 24 deep hybrid heterogenous ensembles that combine the strength of seven deep learning techniques (DenseNet 201, Inception V3, VGG16, VGG19, Inception-ResNet-V3, MobileNet V2 and ResNet 50) for feature extraction and four well-known classifiers (multi-layer perceptron, support vector machines, K-nearest neighbors and decision tree) by means of hard and weighted voting combination methods for histological classification of BC medical image. Furthermore, the best deep hybrid heterogenous ensembles were compared to the deep stacked ensembles to determine the best strategy to design the deep ensemble methods. The empirical evaluations used four classification performance criteria (accuracy, sensitivity, precision and F1-score), fivefold cross-validation, Scott–Knott (SK) statistical test and Borda count voting method. All empirical evaluations were assessed using four performance measures, including accuracy, precision, recall and F1-score, and were over the histological BreakHis public dataset with four magnification factors (40×, 100×, 200× and 400×). SK statistical test and Borda count were also used to cluster the designed techniques and rank the techniques belonging to the best SK cluster, respectively.

Results showed that the deep hybrid heterogenous ensembles outperformed both their singles and the deep stacked ensembles and reached the accuracy values of 96.3, 95.6, 96.3 and 94 per cent across the four magnification factors 40×, 100×, 200× and 400×, respectively.

The proposed deep hybrid heterogenous ensembles can be applied for the BC diagnosis to assist pathologists in reducing the missed diagnoses and proposing adequate treatments for the patients.