Manipulation models for electoral systems are a core research theme in social choice theory; they include bribery (unweighted, weighted, swap, shift, . . . ), control (by adding or deleting voters or candidates), lobbying in referenda and others. We develop a unifying framework for manipulation models with few types of people, one of the most commonly studied scenarios. A critical insight of ou...

Concept drift occurs when a target concept changes over time. I present a new method for learning shifting target concepts during concept drift. The method, called Concept Drift Committee (CDC), uses a weighted committee of hypotheses that votes on the current classification. When a committee member’s voting record drops below a minimal threshold, the member is forced to retire. A new committee...

In this paper, we present LEARN++, a new algorithm that allows existing classifiers to learn incrementally from new data without forgetting previously acquired knowledge. LEARN++ is based on generating multiple classifiers, each trained with a different subset of the training data and then combining them to form an ensemble of classifiers using weighted majority voting. The fundamental contribu...

Concept drift has potential in smart grid analysis because the socio-economic behaviour of consumers is not governed by the laws of physics. Likewise there are also applications in wind power forecasting. In this paper we present decision tree ensemble classification method based on the Random Forest algorithm for concept drift. The weighted majority voting ensemble aggregation rule is employed...

We have previously introduced an incremental learning algorithm Learn(++), which learns novel information from consecutive data sets by generating an ensemble of classifiers with each data set, and combining them by weighted majority voting. However, Learn(++) suffers from an inherent "outvoting" problem when asked to learn a new class omega(new) introduced by a subsequent data set, as earlier ...

Bertino, Ferrari and Atluri (BFA) have recently presented a model for specifying and enforcing authorization constraints for Workflow Management Systems (WFMS). The model is comprehensive and exhibits strong properties such as (1) a language to express constraints, (2) formal notions of constraint consistency and (3) algorithms for role-task and user-task assignments. In this paper, we extend t...

We offer a bargaining model for weighted voting games that is a close relative of the nucleolus and the kernel. We look for a set of weights that preserves winning coalitions that has the property of minimizing the difference between the weight of the smallest and the weight of the largest Minimum Winning Coalition. We claim that such a set of weights provides an a priori measure of a weighted ...

This paper presents a multi-objective Particle Swarm Optimization (PSO) algorithm for worker assignment and mixed-model assembly line balancing problem when task times depend on the worker’s skill level. The objectives of this model are minimization of the number of stations (equivalent to the maximization of the weighted line efficiency), minimization of the weighted smoothness index and minim...