RDPVR: Random Data Partitioning with Voting Rule for Machine Learning from Class-Imbalanced Datasets

Since most classifiers are biased toward the dominant class, class imbalance is a challenging problem in machine learning. The most popular approaches to solving this problem include oversampling minority examples and undersampling majority examples. Oversampling may increase the probability of overfitting, whereas undersampling eliminates examples that may be crucial to the learning process. We present a linear time resampling method based on random data partitioning and a majority voting rule to address both concerns, where an imbalanced dataset is partitioned into a number of small subdatasets, each of which must be class balanced. After that, a specific classifier is trained for each subdataset, and the final classification result is established by applying the majority voting rule to the results of all of the trained models. We compared the performance of the proposed method to some of the most well-known oversampling and undersampling methods, employing a range of classifiers, on 33 benchmark machine learning class-imbalanced datasets. The classification results produced by the classifiers employed on the generated data by the proposed method were comparable to most of the resampling methods tested, with the exception of SMOTEFUNA, which is an oversampling method that increases the probability of overfitting. The proposed method produced results that were comparable to the Easy Ensemble (EE) undersampling method. As a result, for solving the challenge of machine learning from class-imbalanced datasets, we advocate using either EE or our method.

Do supermajority rules really deter extremism? the role of electoral competition

This study investigates how supermajority rules in a legislature affect electoral competition. We construct an extensive-form game wherein parties choose policy platforms in an election. Post election, the policy is determined based on a legislative voting rule. At symmetric equilibrium, supermajority rules induce divergence of policy platforms if and only if the parties are sufficiently attached to their preferred platform. Thus, supermajority rules may not always lead to moderate policies once electoral competition is considered.

Design of expert active knn classifier algorithm using flow stroop colour word test to assess flow state

Flow state assessment is essential to understand the involvement of an individual in a particular task assigned. If there is no involvement in the task assigned then the individual in due course of time gets affected either by psychological or physiological illnesses. The National Crime Records Bureau (NCRB) statistics show that non-involvement in the task drive the individual to a depression state and subsequently attempt for suicide. Therefore, it is essential to determine the decrease in flow level at an earlier stage and take remedial steps to recover them. There are many invasive methods to determine the flow state, which is not preferred and the commonly used non-invasive method is the questionnaire and interview method, which is the subjective and retroactive method, and hence chance to fake the result is more. Hence, the main objective of our work is to design an efficient flow level measurement system that measures flow in an objective method and also determines real-time flow classification. The accuracy of classification is achieved by designing an Expert Active k-Nearest Neighbour (EAkNN) which can classify the individual flow state towards the task assigned into nine states using non-invasive physiological Electrocardiogram (ECG) signals. The ECG parameters are obtained during the performance of FSCWT. Thus this work is a combination of psychological theory, physiological signals and machine learning concepts. The classifier is designed with a modified voting rule instead of the default majority voting rule, in which the contribution probability of nearest points to new data is considered. The dataset is divided into two sets, training dataset 75%and testing dataset 25%. The classifier is trained and tested with the dataset and the classification efficiency is 95%.

Smart contract-based secure cooperative spectrum sensing algorithm

Spectrum sensing is the key technology of cognitive radio. In this article, we apply blockchain technology in spectrum sensing process and propose a related algorithm based on reputation. The algorithm builds a system model based on smart contract in blockchain and applies blockchain asymmetric encryption algorithm and digital signature technology in the process of secondary users’ transmitting local judgments to the secondary user base station. The algorithm can resist spectrum sensing data falsification (SSDF) attack launched by malicious users. This article comprehensively considers the channel error rate, detection probability, secondary user base station budget and remaining energy of the secondary users (SUs) and then establishes the SU’s utility function as well as the game model. By solving the Nash equilibrium, the SU determines whether it uploads sensing data. Finally, the SU base station selects registered SUs by calculating and updating their reputation, obtaining the final judgment by voting rule. With simulations, we prove that the algorithm proposed in this article increases the accuracy and security of spectrum sensing and can effectively resist SSDF attack.

CT classification model of pancreatic serous cystic neoplasms and mucinous cystic neoplasms based on a deep neural network

Background At present, numerous challenges exist in the diagnosis of pancreatic SCNs and MCNs. After the emergence of artificial intelligence (AI), many radiomics research methods have been applied to the identification of pancreatic SCNs and MCNs. Purpose A deep neural network (DNN) model termed Multi-channel-Multiclassifier-Random Forest-ResNet (MMRF-ResNet) was constructed to provide an objective CT imaging basis for differential diagnosis between pancreatic serous cystic neoplasms (SCNs) and mucinous cystic neoplasms (MCNs). Materials and methods This study is a retrospective analysis of pancreatic unenhanced and enhanced CT images in 63 patients with pancreatic SCNs and 47 patients with MCNs (3 of which were mucinous cystadenocarcinoma) confirmed by pathology from December 2010 to August 2016. Different image segmented methods (single-channel manual outline ROI image and multi-channel image), feature extraction methods (wavelet, LBP, HOG, GLCM, Gabor, ResNet, and AlexNet) and classifiers (KNN, Softmax, Bayes, random forest classifier, and Majority Voting rule method) are used to classify the nature of the lesion in each CT image (SCNs/MCNs). Then, the comparisons of classification results were made based on sensitivity, specificity, precision, accuracy, F1 score, and area under the receiver operating characteristic curve (AUC), with pathological results serving as the gold standard. Results Multi-channel-ResNet (AUC 0.98) was superior to Manual-ResNet (AUC 0.91).CT image characteristics of lesions extracted by ResNet are more representative than wavelet, LBP, HOG, GLCM, Gabor, and AlexNet. Compared to the use of three classifiers alone and Majority Voting rule method, the use of the MMRF-ResNet model exhibits a better evaluation effect (AUC 0.96) for the classification of the pancreatic SCNs and MCNs. Conclusion The CT image classification model MMRF-ResNet is an effective method to distinguish between pancreatic SCNs and MCNs. Graphic abstract

Borda, Cancellation and Belief Merging

In this work, we explore the links between the Borda voting rule and belief merging operators. More precisely, we define two families of merging operators inspired by the definition of the Borda voting rule. We also introduce a notion of cancellation in belief merging, inspired by the axiomatization of the Borda voting rule proposed by Young. This allows us to provide a characterization of the drastic merging operator.

Policy Experimentation in Committees: A Case against Veto Rights under Redistributive Constraints

We study optimal policy experimentation by a committee. We consider a dynamic bargaining game in which committee members choose either a risky reform or a safe alternative each period. When no redistribution is allowed, the unique equilibrium outcome is generically inefficient. When redistribution is allowed (even small amounts), there always exists an equilibrium that supports optimal experimentation for any voting rule without veto players. With veto players, however, optimal policy experimentation is possible only with a sufficient amount of redistribution. We conclude that veto rights are more of an obstacle to optimal policy experimentation than are the constraints on redistribution themselves. (JEL D72, C78, H23, D78, D71)

Winner Determination and Strategic Control in Conditional Approval Voting

Our work focuses on a generalization of the classic Minisum approval voting rule, introduced by Barrot and Lang (2016), and referred to as Conditional Minisum (CMS), for multi-issue elections. Although the CMS rule provides much higher levels of expressiveness, this comes at the expense of increased computational complexity. In this work, we study further the issue of efficient algorithms for CMS, and we identify the condition of bounded treewidth (of an appropriate graph that emerges from the provided ballots), as the necessary and sufficient condition for polynomial algorithms, under common complexity assumptions. Additionally we investigate the complexity of problems related to the strategic control of such elections by the possibility of adding or deleting either voters or alternatives. We exhibit that in most variants of these problems, CMS is resistant against control.

Even More Effort Towards Improved Bounds and Fixed-Parameter Tractability for Multiwinner Rules

Multiwinner elections have proven to be a fruitful research topic with many real world applications. We contribute to this line of research by improving the state of the art regarding the computational complexity of computing good committees. More formally, given a set of candidates C, a set of voters V, each ranking the candidates according to their preferences, and an integer k; a multiwinner voting rule identifies a committee of size k, based on these given voter preferences. In this paper we consider several utilitarian and egailitarian OWA (ordered weighted average) scoring rules, which are an extensively researched family of rules (and a subfamily of the family of committee scoring rules). First, we improve the result of Betzler et al. [JAIR, 2013], which gave a O(n^n) algorithm for computing winner under the Chamberlin Courant rule (CC), where n is the number of voters; to a running time of O(2^n), which is optimal. Furthermore, we study the parameterized complexity of the Pessimist voting rule and describe a few tractable and intractable cases. Apart from such utilitarian voting rules, we extend our study and consider egalitarian median and egalitarian mean (both committee scoring rules), showing some tractable and intractable results, based on nontrivial structural observations.