Papers by Andrzej Skowron
Lecture Notes in Computer Science, 2006
Pattern Recognition Letters, 2015
Lecture Notes in Computer Science, 2005
Lecture Notes in Computer Science, 2001
Lecture Notes in Computer Science, 2004
We study properties of infomorphisms between information systems. In particular, we interpret inf... more We study properties of infomorphisms between information systems. In particular, we interpret infomorphisms between information systems in terms of sums with constraints (constrained sums, for short) that are some operations on information systems. Applications of approximation spaces, used in rough set theory, to study properties of infomorphisms are included.
Lecture Notes in Computer Science, 2004
Lecture Notes in Computer Science, 2005
Approximation spaces are fundamental for the rough set approach. We discuss their application in ... more Approximation spaces are fundamental for the rough set approach. We discuss their application in machine learning and pattern recognition.
Rough Sets and Current Trends in Computing, 2002
Lecture Notes in Computer Science, 2014
As far as the laws of mathematics refer to reality, they are not certain; and as far as they are ... more As far as the laws of mathematics refer to reality, they are not certain; and as far as they are certain, they do not refer to reality.-Albert Einstein ([2]) Constructing the physical part of the theory and unifying it with the mathematical part should be considered as one of the main goals of statistical learning theory-Vladimir Vapnik
Natural Computing, 2015
Understanding the nature of interactions is regarded as one of the biggest challenges in projects... more Understanding the nature of interactions is regarded as one of the biggest challenges in projects related to complex adaptive systems. We discuss foundations for interactive computations in interactive intelligent systems (IIS), developed in the Wistech program and used for modeling complex systems. We emphasize the key role of risk management in problem solving by IIS. The considerations are based on experience gained in real-life projects concerning, e.g., medical diagnosis and therapy support, control of an unmanned helicopter, fraud detection algorithmic trading or fire commander decision support. Keywords Rough sets Á Granular computing Á Interactive computations Á Interactive intelligent systems Á Risk management Á Wisdom Technology Mathematics Subject Classification 68T05 Á 68T27 Á 68T37-To Roman Swiniarski in Memoriam Traditional statistics is strong in devising ways of describing data and inferring distributional parameters from sample. Causal inference requires two additional ingredients: a science-friendly language for articulating causal knowledge, and a mathematical machinery for processing that knowledge, combining it with data and drawing new causal conclusions about a phenomenon.
Intelligent Systems Reference Library, 2013
Theoretical Computer Science, 2011
Rough sets and fuzzy sets in natural computing Natural Computing (NC) is a discipline that builds... more Rough sets and fuzzy sets in natural computing Natural Computing (NC) is a discipline that builds a bridge between computer science and natural sciences. It deals mainly with the methodologies and models that take inspiration from nature (or are based on natural phenomena) for problemsolving, using computers (or computational techniques) to synthesize natural phenomena, or employ natural materials (e.g., molecules) for computation. The constituent technologies for performing these tasks include cellular automata, artificial neural networks (ANN), evolutionary algorithms, swarm intelligence, artificial immune systems, fractal geometry, artificial life, DNA computing, granular computing and perception-based computing. For example, artificial neural networks attempt to emulate the information representation and processing scheme and the discriminatory ability of biological neurons in the human brain together with characteristics such as adaptivity, robustness, ruggedness, speed and optimality. Similarly, evolutionary algorithms create a biologically inspired tool based on powerful metaphors from the natural world. They mimic some of the processes observed in natural evolution such as crossover, selection and mutation, leading to a stepwise optimization of organisms. On the other hand, perception-based computing provides the capability to compute and reason with perceptionbased information as humans do to perform a wide variety of physical and mental tasks without any measurement and computation. Reflecting the finite ability of the sensory organs and (finally the brain) to resolve details, perceptions are inherently fuzzy-granular (f-granular) [21]. That is, boundaries of perceived classes are unsharp and the values of the attributes they can take are granulated (a clump of indistinguishable points or objects) [20,5]. Granulation is also a computing paradigm such as, among others, self-reproduction, self-organization, functioning of the brain, Darwinian evolution, group behavior, cell membranes, and morphogenesis, that are abstracted from natural phenomena. A good survey on natural computing explaining its different facets is provided in [4]. Granulation is inherent in human thinking and reasoning processes. Granular computing (GrC) provides an information processing framework where computation and operations are performed on information granules, and is based on the realization that precision is sometimes expensive and not much meaningful in modeling and controlling complex systems. When a problem involves incomplete, uncertain, and vague information, it may be difficult to differentiate distinct elements, and so one may find it convenient to consider granules for its handling. The structure of granulation can often be defined using methods based on rough sets, fuzzy sets or their combination. In this consortium, rough sets and fuzzy sets work synergistically, often with other soft computing approaches, and use the principle of granular computing. The developed systems exploit the tolerance for imprecision, uncertainty, approximate reasoning and partial truth under soft computing framework and is capable of achieving tractability, robustness, and close resemblance with human-like (natural) decision-making for pattern recognition in ambiguous situations [19]. Qualitative reasoning and modeling in NC requires to develop methods supporting approximate reasoning under uncertainty about non-crisp, often vague concepts. One of the very general schemes of tasks for such qualitative reasoning can be described as follows. From some basic objects (called patterns, granules or molecules) it is required to construct (induce) complex objects satisfying a given specification (often expressed in natural language specification) to a satisfactory degree. For example, in learning concepts from examples we deal with tasks where partial information about the specification is given by examples and counter examples concerning the classified objects. As instances of such complex objects one can consider classifiers studied in machine learning or data mining, new medicine against some viruses or behavioral patterns of cell interaction induced from interaction of biochemical processes realized in cells. Over the years, we have learned how to solve some of such tasks, however many of them still pose great challenges. One of the reasons for this is that the discovery process of complex objects relevant for the given specification requires multilevel reasoning with the necessity of discovering on each level the relevant structural objects and their properties. The search space for such structural objects and properties is huge and this, in particular, requires fully automatic methods that are not feasible using the existing computing technologies. However, this process can be supported by domain knowledge which can be used for generating hints in the searching process (see, e.g., [1]).
Computations in Rough-Granular Computing (RGC) are performed on (information) granules. The rough... more Computations in Rough-Granular Computing (RGC) are performed on (information) granules. The rough set approach is used in RGC for inducing granules approximating other granules about which imperfect knowledge is given only. For modeling of complex systems, it is important to extend the RGC approach to Interactive Rough-Granular Computing (IRGC) based on interactions of granules. In this paper, we discuss some fundamental issues for interaction of granules such as general scheme of interactions and the role of dynamic attributes and dynamic information systems in modeling interactive computations.
Intelligent Systems Reference Library, 2013
Wolski for their work and help in making this important book available. We extend an expression o... more Wolski for their work and help in making this important book available. We extend an expression of gratitude to Professors Janusz Kacprzyk and Lakhmi C. Jain, to Dr. Thomas Ditzinger and to the Series Intelligent Systems Reference Library staff at Springer for their support in making this book possible.
Lecture Notes in Computer Science, 2005
Fundamenta Informaticae, Apr 1, 2006
International Journal of Approximate Reasoning, 2018
As a supervised learning method, classical rough set theory often requires a large amount of labe... more As a supervised learning method, classical rough set theory often requires a large amount of labeled data, in which concept approximation and attribute reduction are two key issues. With the advent of the age of big data however, labeling data is an expensive and laborious task and sometimes even infeasible, while unlabeled data are cheap and easy to collect. Hence, techniques for rough data analysis in big data using a semi-supervised approach, with limited labeled data, are desirable. Although many concept approximation and attribute reduction algorithms have been proposed in the classical rough set theory, quite often, these methods are unable to work well in the context of limited labeled big data. The challenges to classical rough set theory can be summarized with three issues: limited labeled property of big data, computational inefficiency and over-fitting in attribute reduction. To address these three challenges, we introduce a theoretic framework called local rough set, and develop a series of corresponding concept approximation and attribute reduction algorithms with linear time complexity, which can efficiently and effectively work in limited labeled big data. Theoretical analysis and experimental results show that each of the algorithms in the local rough set significantly outperforms its original counterpart in classical rough set theory. It is worth noting that the performances of the algorithms in the local rough set become more significant when dealing with larger data sets.
Lecture Notes in Computer Science, 2000
Lecture Notes in Computer Science, 2001
This paper introduces a neural network architecture based on rough sets and rough membership func... more This paper introduces a neural network architecture based on rough sets and rough membership functions. The neurons of such networks instantiate approximate reasoning in assessing knowledge gleaned from input data. Each neuron constructs upper and lower approximations as an aid to classifying inputs. Rough neuron output has various forms. In this paper, rough neuron output results from the application of a rough membership function. A brief introduction to the basic concepts underlying rough membership neural networks is given. An application of rough neural computing is briefly considered in classifying the waveforms of power system faults. Experimental results with rough neural classification of waveforms are also given.
Rough Sets and Knowledge Technology, 2015
Decision support in solving problems related to complex systems requires relevant computation mod... more Decision support in solving problems related to complex systems requires relevant computation models for the agents as well as methods for reasoning on properties of computations performed by agents. Agents are performing computations on complex objects [e.g., (behavioral) patterns, classifiers, clusters, structural objects, sets of rules, aggregation operations, (approximate) reasoning schemes]. In Granular Computing (GrC), all such constructed and/or induced objects are called granules. To model interactive computations performed by agents, crucial for the complex systems, we extend the existing GrC approach to Interactive Granular Computing (IGrC) approach by introducing complex granules (c-granules or granules, for short). Many advanced tasks, concerning complex systems, may be classified as control tasks performed by agents aiming at achieving the high-quality computational trajectories relative to the considered quality measures defined over the trajectories. Here, new challenges are to develop strategies to control, predict, and bound the behavior of the system. We propose to investigate these challenges using the IGrC framework. The reasoning, which aims at controlling of computations, to achieve the required targets, is called an adaptive judgement. This reasoning deals with granules and computations over them. Adaptive judgement is more than a mixture of reasoning based on deduction, induction and abduction. Due to the uncertainty the agents generally cannot predict exactly the results of actions (or plans). Moreover, the approximations of the complex vague concepts initiating actions (or plans) are drifting with time. Hence, adaptive strategies for evolving approximations of concepts are needed. In particular, the adaptive judgement is very much needed in the efficiency management of granular computations, carried out by agents, for risk assessment, risk treatment, and cost/benefit analysis. In the paper, we emphasize the role of the rough set-based methods in IGrC. The discussed approach is a step towards realization of the Wisdom Technology (WisTech) program, and is developed over years, based on the work experience on different real-life projects. Keywords Rough set Á (Interactive) granular computing Á Interactive computation Á Adaptive judgement Á Efficiency management Á Risk management Á Cost/benefit analysis Á Big data technology Á Cyber-physical system Á Wisdom web of things Á Ultra-large system 1 Introduction GrC has emerged from many different disciplines and fields, including General Systems Theory, Hierarchy Theory, Social Networks, Artificial Intelligence (AI), Human
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Papers by Andrzej Skowron