Aggregation of classifiers: a justifiable information granularity approach.

In this paper, we introduced a new approach of combining multiple classifiers in a heterogeneous ensemble system. Instead of using numerical membership values when combining, we constructed interval membership values for each class prediction from the meta-data of observation by using the concept of information granule. In the proposed method, the uncertainty (diversity) of the predictions produced by the base classifiers is quantified by the interval-based information granules. The decision model is then generated by considering both bound and length of the intervals. Extensive experimentation using the UCI datasets has demonstrated the superior performance of our algorithm over other algorithms including six fixed combining methods, one trainable combining method, AdaBoost, bagging, and random subspace

Organized Business, Political Regimes and Property Rights across the Russian Federation

This article explores the inter-relationship of collective action within the business community, the nature of the political regime and the security of firms’ property rights. Drawing on a pair of surveys recently administered in Russia, we present evidence that post-communist business associations have begun to coordinate business influence over state actors in a manner that is sensitive to regional politics. A firm’s ability to defend itself from government predation and to shape its institutional environment as well as its propensity to invest in physical capital are strongly related to both its membership in a business association and the level of democratization in its region. Of particular note, the positive effect of association membership on securing property rights increases in less democratic regions. The evidence, that is, suggests that collective action in the business community substitutes for democratic pressure in constraining public officials.

07181 Abstracts Collection -- Parallel Universes and Local Patterns

From 1 May 2007 to 4 May 2007 the Dagstuhl Seminar 07181 ``Parallel Universes and Local Patterns\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Organized business, political regimes and property rights across the Russian Federation

Abstract: This article explores the inter-relationship of collective action within the business community, the nature of the political regime and the security of firms’ property rights. Drawing on a pair of surveys recently administered in Russia, we present evidence that post-communist business associations have begun to coordinate business influence over state actors in a manner that is sensitive to regional politics. A firm’s ability to defend itself from government predation and to shape its institutional environment as well as its propensity to invest in physical capital are strongly related to both its membership in a business association and the level of democratization in its region. Of particular note, the positive effect of association membership on securing property rights increases in less democratic regions. The evidence, that is, suggests that collective action in the business community substitutes for democratic pressure in constraining public officials.collective action; property rights; political institutions; business associations

Uncertainty Management of Intelligent Feature Selection in Wireless Sensor Networks

Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in WSN. Using rough set theory and temporal reasoning a novel formalism has been developed to characterize and quantify the uncertainties in predicting spatio-temporal patterns from sensor data. This research also uncovers the trade-off among the uncertainty measures, which can be used to develop a multi-objective optimization model for real-time decision making in sensor data aggregation and samplin

Performing Feature Selection with ACO

Summary. The main aim of feature selection is to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. In real world problems FS is a must due to the abundance of noisy, irrelevant or misleading features. However, current methods are inadequate at finding optimal reductions. This chapter presents a feature selection mechanism based on Ant Colony Optimization in an attempt to combat this. The method is then applied to the problem of finding optimal feature subsets in the fuzzy-rough data reduction process. The present work is applied to two very different challenging tasks, namely web classification and complex systems monitoring.