Impact Assessment of Qualitative Policy Scenarios; A Comparative Case Study on Land Use in Sicily

The purpose of this paper is to offer a contribution to the study of integrated assessment procedures for evaluating the effectiveness of agri-environmental policy strategies. While in the past the studies in this context have typically concentrated on the contents of methods in isolation, there is a growing trend towards methodological perspectives that support the linking of such methods. The focus here is on the combination of discrete multicriteria approaches used for handling qualitative information in a sequence of steps: the regime method, the evamix method and rough-set analysis. The first two methods will be used to obtain a ranking of four alternative scenarios of agri-environmental policies in a selected area of study, in this case, Sicily. The results obtained are discussed and re-analysed by using the rough-set approach as a recent meta-analytical tool. Finally, the analysis findings are applied to an investigation into the potential effectiveness of agricultural policies in promoting sustainable rural development in Sicily. © 2003, MCB UP Limite

Interval-valued analysis for discriminative gene selection and tissue sample classification using microarray data

AbstractAn important application of gene expression data is to classify samples in a variety of diagnostic fields. However, high dimensionality and a small number of noisy samples pose significant challenges to existing classification methods. Focused on the problems of overfitting and sensitivity to noise of the dataset in the classification of microarray data, we propose an interval-valued analysis method based on a rough set technique to select discriminative genes and to use these genes to classify tissue samples of microarray data. We first select a small subset of genes based on interval-valued rough set by considering the preference-ordered domains of the gene expression data, and then classify test samples into certain classes with a term of similar degree. Experiments show that the proposed method is able to reach high prediction accuracies with a small number of selected genes and its performance is robust to noise

Rough set and rule-based multicriteria decision aiding

The aim of multicriteria decision aiding is to give the decision maker a recommendation concerning a set of objects evaluated from multiple points of view called criteria. Since a rational decision maker acts with respect to his/her value system, in order to recommend the most-preferred decision, one must identify decision maker's preferences. In this paper, we focus on preference discovery from data concerning some past decisions of the decision maker. We consider the preference model in the form of a set of "if..., then..." decision rules discovered from the data by inductive learning. To structure the data prior to induction of rules, we use the Dominance-based Rough Set Approach (DRSA). DRSA is a methodology for reasoning about data, which handles ordinal evaluations of objects on considered criteria and monotonic relationships between these evaluations and the decision. We review applications of DRSA to a large variety of multicriteria decision problems

Identification of Biodiversity and Other Forest Attributes for Sustainable Forest Management: Siberian Forest Case Study

This paper attempts to identify characteristics of biodiversity and other (forest) ecosystem conditions that are considered essential for a description of ecosystem functioning and development of sustainable forest management practices in the Siberian forests. This is accomplished through an analysis of net primary production of phytomass (NPP) which acts as a proxy for ecosystem functioning. Rough Sets (RS) analysis is applied to study the Siberian ecoregions classified into compact and cohesive NPP performance classes. Through a heuristic procedure, a reduced set of attributes is generated for a NPP classification problem. In order to interpret relationships between various forest characteristics, so-called "interesting rules" are generated on a basis of reduced problem description. These "interesting rules" provide means to draw conclusions in the form of knowledge statements about functioning of the Siberian forests

Rough sets, their extensions and applications

Rough set theory provides a useful mathematical foundation for developing automated computational systems that can help understand and make use of imperfect knowledge. Despite its recency, the theory and its extensions have been widely applied to many problems, including decision analysis, data-mining, intelligent control and pattern recognition. This paper presents an outline of the basic concepts of rough sets and their major extensions, covering variable precision, tolerance and fuzzy rough sets. It also shows the diversity of successful applications these theories have entailed, ranging from financial and business, through biological and medicine, to physical, art, and meteorological

Exploring the Technology Input and Economy Output in Chinese National Innovation Demonstration Zone Based on Rough Set Theory

The previous study about the relationship between technology input and economy output was mainly concentrated on their linear or functional formulation, while little on the data independencies between them. This study explored the data independencies between technology input and economy output of Chinese National Innovation Demonstration Zone based on Rough Sets Theory, for the purpose of conducting a new way to understand the unstructured relation between technology input and economy output, as well as to promoting the effective combination of technology and economy output of Chinese National Innovation Demonstration Zone, which was the most important part of national innovation system of China. The Rough Set Theory was applied to analyze the 8 Chinese National Innovation Demonstration Zone’s technology input and economy output data from 2007 to 2014. The result demonstrated that: (1) of the economy output indicators, ratio of technical income to total income, ratio of net profit to total income and export were not combined effectively with the technology input, while total income, technical income, net profit and taxes submitted had been combined with the technology input very significantly; (2) of the technology input indicators, all of them had shown the linkage with economy output indicators significantly, and expenditure on R&D activities was the most important one; (3) an two factor theory effect might existed between the technology input and economy output, senior and middle level professional qualifications, personal engaged in R&D activities and expenditure on R&D activities were the hygiene factors, ratio of expenditure on R&D activities to personal engaged in R&D activities and ratio of expenditure on R&D activities to total income were motivation factors. Keywords: Chinese National Innovation Demonstration Zone, Technology input indicators, Economy output indicators, Combination effectiveness, Rough Set Theor