Detecting Topology Variations in Dynamical Networks

This paper considers the problem of detecting topology variations in dynamical networks. We consider a network whose behavior can be represented via a linear dynamical system. The problem of interest is then that of finding conditions under which it is possible to detect node or link disconnections from prior knowledge of the nominal network behavior and on-line measurements. The considered approach makes use of analysis tools from switching systems theory. A number of results are presented along with examples

Bankruptcy Prediction with Rough Sets

The bankruptcy prediction problem can be considered an or dinal classification problem. The classical theory of Rough Sets describes objects by discrete attributes, and does not take into account the order- ing of the attributes values. This paper proposes a modification of the Rough Set approach applicable to monotone datasets. We introduce re- spectively the concepts of monotone discernibility matrix and monotone (object) reduct. Furthermore, we use the theory of monotone discrete functions developed earlier by the first author to represent and to com- pute decision rules. In particular we use monotone extensions, decision lists and dualization to compute classification rules that cover the whole input space. The theory is applied to the bankruptcy prediction problem

Geographic Information Systems and Decision Processes for Urban Planning: A Case Study of Rough Set Analysis on the Residential Areas of the City of Cagliari, Italy

In Italy, urban planning is based on the city Masterplan. This plan identifies the future urban organization and a system of zoning rules. Land-use policies are based on these rules. The zoning rules should synthesize environmental and spatial knowledge and policy decisions concerning the possible futures, with reference to the different urban functions. In this essay, a procedure of analysis of the city Masterplan of Cagliari, the regional capital city of Sardinia (Italy), is discussed and applied. This procedure is referred to the residential areas. The procedure tries to explain the urban organization of the housing areas using a system of variables based on the integration of different branches of knowledge concerning the urban environment. The decisions on the urban futures that the zoning rules entail are critically analyzed in terms of consistency with this knowledge system. The procedure consists of two phases. In the first phase, the urban environment is analyzed and described. This is done by defining and developing a geographic information system. This system utilizes a spatial analysis approach to figure out the integration of the residential areas into the urban fabric. The second phase is inferential. Based on the geographic information system developed in the first phase, a knowledge discovery in databases (KDD) technique, the rough set analysis (RSA), is applied. This technique allows to recognize the connection patterns between the urban knowledge system and the city planning decisions. The patterns, the decision rules, which come from the RSA implementation are important starting points for further investigation on the development of decision models concerning urban planning.

Algorithms for solving the reducts problem in rough sets

This work deals with finding minimal reducts of decision table based on the rough sets theory. Its goal is to develop algorithms capable of finding such reducts. Two algorithms are presented in this report: the first based on Boolean reasoning function, the second based on Genetic Algorithm. Test results on real data are given and conclusions are made

Multiple Relevant Feature Ensemble Selection Based on Multilayer Co-Evolutionary Consensus MapReduce

IEEE Although feature selection for large data has been intensively investigated in data mining, machine learning, and pattern recognition, the challenges are not just to invent new algorithms to handle noisy and uncertain large data in applications, but rather to link the multiple relevant feature sources, structured, or unstructured, to develop an effective feature reduction method. In this paper, we propose a multiple relevant feature ensemble selection (MRFES) algorithm based on multilayer co-evolutionary consensus MapReduce (MCCM). We construct an effective MCCM model to handle feature ensemble selection of large-scale datasets with multiple relevant feature sources, and explore the unified consistency aggregation between the local solutions and global dominance solutions achieved by the co-evolutionary memeplexes, which participate in the cooperative feature ensemble selection process. This model attempts to reach a mutual decision agreement among co-evolutionary memeplexes, which calls for the need for mechanisms to detect some noncooperative co-evolutionary behaviors and achieve better Nash equilibrium resolutions. Extensive experimental comparative studies substantiate the effectiveness of MRFES to solve large-scale dataset problems with the complex noise and multiple relevant feature sources on some well-known benchmark datasets. The algorithm can greatly facilitate the selection of relevant feature subsets coming from the original feature space with better accuracy, efficiency, and interpretability. Moreover, we apply MRFES to human cerebral cortex-based classification prediction. Such successful applications are expected to significantly scale up classification prediction for large-scale and complex brain data in terms of efficiency and feasibility