Classifiers Based on Two-Layered Learning

Abstract. In this paper we present an exemplary classifier (classifica-tion algorithm) based on two-layered learning. In the first layer of learn-ing a collection of classifiers is induced from a part of original training data set. In the second layer classifiers are induced using patterns ex-tracted from already constructed classifiers on the basis of their perfor-mance on the remaining part of training data. We report results of exper-iments performed on the following data sets, well known from literature: diabetes, heart disease, australian credit (see [5]) and lymphography (see [4]). We compare the standard rough set method used to induce classi-fiers (see [1] for more details), based on minimal consistent decision rules (see [6]), with the classifier based on two-layered learning.

Between Apprehension and Support: Social Dialogue, Democracy, and Industrial Restructuring in Central and Eastern Europe

This article explores the attitudes of trade union organizations to restructuring and privatization of their enterprises to strategic foreign investors in Central and Eastern Europe\u27s biggest steel producers: Poland, Czech Republic, Romania, and Slovakia. Contrary to advocates of insulating technocratic decision-makers from social partners, this article argues that higher quality of democracy and concomitant social dialogue carried out at the level of the sector with union organizations that are autonomous of the government in power (as was the case in the Czech Republic and Poland), are associated with greater restructuring and with support for privatization to strategic foreign investors. In these circumstances, the unions actually pressure reluctant governments to accelerate the privatization process. By contrast, politically motivated capture of individual enterprise-level unions and splitting them from sectoral-level organizations, as occurred in countries with lower quality of democracy (Romania and Slovakia), weakens the autonomous sectoral-level organizations, which are generally supportive of restructuring. Conversely, captured unions remain far more resistant to reform than their counterparts belonging to autonomous sectoral organizations. Thus, higher quality of democracy and concomitant vibrant social dialogue safeguard industrial restructuring

The persistence of cliques in the post-communist state. The case of deniability in drug reimbursement policy in Poland

This article explores a key question in political sociology: Can post-communist policy-making be described with classical theories of the Western state or do we need a theory of the specificity of the post-communist state? In so doing, we consider Janine Wedel's clique theory, concerned with informal social actors and processes in post-communist transition. We conducted a case study of drug reimbursement policy in Poland, using 109 stakeholder interviews, official documents and media coverage. Drawing on 'sensitizing concepts' from Wedel's theory, especially the notion of 'deniability', we developed an explanation of why Poland's reimbursement policy combined suboptimal outcomes, procedural irregularities with limited accountability of key stakeholders. We argue that deniability was created through four main mechanisms: (1) blurred boundaries between different types of state authority allowing for the dispersion of blame for controversial policy decisions; (2) bridging different sectors by 'institutional nomads', who often escaped existing conflicts of interest regulations; (3) institutional nomads' 'flexible' methods of influence premised on managing roles and representations; and (4) coordination of resources and influence by elite cliques monopolizing exclusive policy expertise. Overall, the greatest power over drug reimbursement was often associated with lowest accountability. We suggest, therefore, that the clique theory can be generalized from its home domain of explanation in foreign aid and privatizations to more technologically advanced policies in Poland and other post-communist countries. This conclusion is not identical, however, with arguing the uniqueness of the post-communist state. Rather, we show potential for using Wedel's account to analyse policy-making in Western democracies and indicate scope for its possible integration with the classical theories of the state.</p

Governing drug reimbursement policy in Poland: The role of the state, civil society, and the private sector

This article investigates the distribution of power in Poland’s drug reimbursement policy in the early 2000s. We examine competing theoretical expectations suggested by neopluralism, historical institutionalism, corporate domination, and clique theory of the post-communist state, using data from a purposive sample of 109 semi-structured interviews and documentary sources. We have four concrete findings. First, we uncovered rapid growth in budgetary spending on expensive drugs for narrow groups of patients. Second, to achieve these favorable policy outcomes drug companies employed two prevalent methods of lobbying: informal persuasion of key members of local cliques and endorsements expressed by patient organizations acting as seemingly independent “third parties.” Third, medical experts were co-opted by multinational drug companies because they relied on these firms for scientific and financial resources that were crucial for their professional success. Finally, there was one-way social mobility from the state to the pharmaceutical sector, not the “revolving door” pattern familiar from advanced capitalist countries, with deleterious consequences for state capacity. Overall, the data best supported a combination of corporate domination and clique theory: drug reimbursement in Poland was dominated by Western multinationals in collaboration with domestically based cliques.Piotr Ozieranski is indebted to the Department of Sociology, University of Cambridge and St Edmund’s College for research grants

Re-stating Party Development in Central and Eastern Europe: A Response

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Diagnosing skin melanoma: current versus future directions

A new database containing 410 cases of nevi pigmentosi, in four categories: benign nevus, blue nevus, suspicious nevus and melanoma malignant, carefully verified by histopathology, is described. The database is entirely different from the base presented previously, and can be readily used for research based on the so-called constructive induction in machine learning. To achieve this, the database features a different set of thirteen descriptive attributes, with a fourteenth additional attribute computed by applying values of the remaining thirteen attributes. In addition, a new program environment for the validation of computer-assisted diagnosis of melanoma, is briefly discussed. Finally, results are presented on determining optimal coefficients for the well-known ABCD formula, useful for melanoma diagnosis