Reception of subjectivity of critical theory and Machiavellianism : a proposal for computer aided diagnosis of pathology in education

The authors of this paper, while observing the Polish education system, can not accept the ossified administrative structures existing in the system and the triumph of economic objectives over the human ones, the latter understood as individuals having the opportunity for self-realisation (developing one's subjectivity). We want to ask what identities an education system functioning in this way would create? A lot of attention is devoted in the literature to the problem of education. We also want to speak on this matter, for the sake of the common good which education undoubtedly is. We sincerely hope that the perception of problems in the system will lead to its repair and not its destruction. This paper consists of two main parts. The first part presents the reconstruction of critical theory based on the philosophy of Theodor Adorno, Max Horkheimer and Jurgen Habermas, which concerns the concept of subjectivity. The notion of subjectivity emerging from the thoughts of the representatives of the Frankfurt School is then confronted with the image of man created by the Machiavellian thought, according to social sciences. This is followed by a description of the Machiavellian personality trait, based on the psychological interpretation of Machiavellianism, and not on the current philosophical interpretation. In the second part, the authors present the proposal for using computer exploration methods to identify Machiavellian behaviours. The proposed solution takes into account cognitive, educational and preventive aspects

Preference learning based decision map algebra: specification and implementation

Decision Map Algebra (DMA) is a generic and context independent algebra, especially devoted to spatial multicriteria modelling. The algebra defines a set of operations which formalises spatial multicriteria modelling and analysis. The main concept in DMA is decision map, which is a planar subdivision of the study area represented as a set of non-overlapping polygonal spatial units that are assigned, using a multicriteria classification model, into an ordered set of classes. Different methods can be used in the multicriteria classification step. In this paper, the multicriteria classification step relies on the Dominance-based Rough Set Approach (DRSA), which is a preference learning method that extends the classical rough set theory to multicriteria classification. The paper first introduces a preference learning based approach to decision map construction. Then it proposes a formal specification of DMA. Finally, it briefly presents an object oriented implementation of DMA

Condition attributes, properties of decision rules, and discretisation : analysis of relations and dependencies

When mining of input data is focused on rule induction, knowledge, discovered in exploration of existing patterns, is stored in combinations of certain conditions on attributes included in rule premises, leading to specific decisions. Through their properties, such as lengths, supports, cardinalities of rule sets, inferred rules characterise relations detected among variables. The paper presents research dedicated to analysis of these dependencies, considered in the context of various discretisation methods applied to the input data from stylometric domain. For induction of decision rules from data, Classical Rough Set Approach was employed. Next, based on rule properties, several factors were proposed and evaluated, reflecting characteristics of available condition attributes. They allowed to observe how variables and rule sets changed depending on applied discretisation algorithms

Decision rules construction : algorithm based on EAV model

In the paper, an approach for decision rules construction is proposed. It is studied from the point of view of the supervised machine learning task, i.e., classification, and from the point of view of knowledge representation. Generated rules provide comparable classification results to the dynamic programming approach for optimization of decision rules relative to length or support. However, the proposed algorithm is based on transformation of decision table into entity– attribute–value (EAV) format. Additionally, standard deviation function for computation of averages’ values of attributes in particular decision classes was introduced. It allows to select from the whole set of attributes only these which provide the highest degree of information about the decision. Construction of decision rules is performed based on idea of partitioning of a decision table into corresponding subtables. In opposite to dynamic programming approach, not all attributes need to be taken into account but only these with the highest values of standard deviation per decision classes. Consequently, the proposed solution is more time efficient because of lower computational complexity. In the framework of experimental results, support and length of decision rules were computed and compared with the values of optimal rules. The classification error for data sets from UCI Machine Learning Repository was also obtained and compared with the ones for dynamic programming approach. Performed experiments show that constructed rules are not far from the optimal ones and classification results are comparable to these obtained in the framework of the dynamic programming extension

Selected approaches for decision rules construction-comparative study

Decision rules are popular form of knowledge representation. From this point of view, length of such rules is an important factor since it influences on data understanding by experts. Unfortunately, the problem of construction of short rules is NP-hard, so different heuristics are discussed in the literature. The paper presents comparison of two selected methods for decision rules construction. The first one is connected with a new algorithm based on EAV model, the second one - with construction of rules based on reduct. Decision rules were induced for data sets from UCI ML Repository and compared from the point of view of length and support, and from the point of view of classification accuracy. Results of Wilcoxon test are also included

Spare parts classification in industrial manufacturing using the dominance-based rough set approach

Classification is one of the critical issues in the operations management of spare parts. The issue of managing spare parts involves multiple criteria to be taken into consideration, and therefore, a number of approaches exists that consider criteria such as criticality, price, demand, lead time, and obsolescence, to name a few. In this paper, we first review proposals to deal with inventory control. We then propose a three-phase multicriteria classification framework for spare parts management using the dominance-based rough set approach (DRSA). In the first phase, a set of ‘if–then’ decision rules is generated from historical data using the DRSA. The generated rules are then validated in the second phase by using both the automated and manual approaches, including cross-validation and feedback assessments by the decision maker. The third and final phase is to classify an unseen set of spare parts in a real setting. The proposed approach has been successfully applied to data collected from a manufacturing company in China. The proposed framework was practically tested on different spare parts and, based on the feedback received from the industry experts, 96% of the spare parts were correctly classified. Furthermore, the cross-validation results show that the proposed approach significantly outperforms other well-known classification methods. The proposed approach has several important characteristics that distinguish it from existing ones: (i) it is a learning-set based analysis approach; (ii) it uses a powerful multicriteria classification method, namely the DRSA; (iii) it validates the generated decision rules with multiple strategies; and (iv) it actively involves the decision maker during all the steps of the decision making process

Natural Environment Management and Applied Systems Analysis

This volume contains papers from the NEMASA Konan-IIASA Joint Workshop on Natural Environment Management and Applied Systems Analysis, which took place at IIASA September 6-8, 2000. The workshop was an activity of the research project "Modeling by Computational Intelligence and its Application to Natural Environment Management." The project is being supported by the Hirao Taro Foundation of the Konan University Association for Academic Research, Kobe, Japan. The management of the natural environment -- in particular, the use of advanced agricultural practices -- poses a major challenge to modern society, but perhaps applied systems analysis can help. The workshop set was about to: present new concepts and methodologies for managing the environment, and offer an open forum for the exchange of ideas among research disciplines, especially between agro-environmental and applied systems analysis research and between researchers and practitioners. The paper deal with a range of topics. The editors have arranged them into the following categories: (1) modeling methodologies, (2) data analysis, (3) land use, (4) water management, and (5) applications