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Zdzisław Pawlak

Bio: Zdzisław Pawlak is an academic researcher from Polish Academy of Sciences. The author has contributed to research in topics: Rough set & Dominance-based rough set approach. The author has an hindex of 49, co-authored 214 publications receiving 28434 citations. Previous affiliations of Zdzisław Pawlak include Warsaw University of Technology & University of Warsaw.


Papers
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Book ChapterDOI
31 Aug 2005
TL;DR: It is shown that flow graph can be used both as formal language for computing approximations of sets in the sense of rough set theory, and as description tool for data structure, to this end decision algorithm induced by the flow graph is defined and studied.
Abstract: This paper concerns the relationship between rough sets and flow graphs. It is shown that flow graph can be used both as formal language for computing approximations of sets in the sense of rough set theory, and as description tool for data structure. This description is employed next for finding patterns in data. To this end decision algorithm induced by the flow graph is defined and studied.

43 citations

Journal ArticleDOI
01 Aug 2000-Infor
TL;DR: In this paper basic concepts of rough set theory will be given and its significance for decision analysis will be briefly discussed.
Abstract: Rough set theory is a new mathematical approach to vagueness and uncertainty. The theory has found many real life applications world wide. It is also considered as a very well suited new mathematical tool to deal with various decision problems and many papers on rough set theory and decision support have been published recently. Rough set theory gives new insight into the decision process and offers new efficient algorithms. Several real life decision problems have been successfully solved using this approach. In this paper basic concepts of rough set theory will be given and its significance for decision analysis will be briefly discussed.

43 citations

Book ChapterDOI
TL;DR: This article is intended to present some philosophical observations rather than to consider technical details or applications of rough set theory, and refrain from presentation of many interesting applications and some generalizations of the theory.
Abstract: This article presents some general remarks on rough sets and their place in general picture of research on vagueness and uncertainty – concepts of utmost interest, for many years, for philosophers, mathematicians, logicians and recently also for computer scientists and engineers particularly those working in such areas as AI, computational intelligence, intelligent systems, cognitive science, data mining and machine learning. Thus this article is intended to present some philosophical observations rather than to consider technical details or applications of rough set theory. Therefore we also refrain from presentation of many interesting applications and some generalizations of the theory.

43 citations

01 Jan 1996
TL;DR: Rough set theory is a relatively new mathematical and Al technique introduced in the early 1980's by Pawlak that has found many real life applications in many areas, including data and decision analysis, databases, knowledge based systems, and machine learning.
Abstract: The two most significant developments in the field of artificial intelligence (Al) since 1990 are real world practicality and diversification [14,15]. Fuzzy set theory, for example, has grown to become a major scientific technology, applied to a couple of thousand systems for everyday industrial and commercial settings worldwide [16]. The area of neural networks is another example where extensive practical applications are expected [27]. An interesting question would be determining what the next generation of new Al technologies are and their potentials for practical applications. Rough set theory is a relatively new mathematical and Al technique introduced in the early 1980's by Pawlak. The technique is particularly suited to reasoning about imprecise or incomplete data, and discovering relationships in this data. The main advantage of rough set theory is that it does not require any preliminary or additional information about data like probability in statistics, basic probability assignment in Dempster Shafer theory of evidence or the value of possibility in fuzzy set theory. Rough set theory overlaps, to some extend, with many other theories used to reasoning about data, in particular with Dempster Shafer theory and fuzzy set theory. Despite of these connections it can be viewed as an independent discipline in its own right. Recently there has been a growing interest in rough set theory among researchers in many Al related disciplines. The theory has found many real life applications in many areas. The primary application of rough sets so far have been in data and decision analysis, databases, knowledge based systems, and machine learning. Some information about application of the theory can be found in [20]. Although there has been some research on rough control [4,8,9,10,11,12,13,23,25,27,28,29] their number and domains have been relatively small, and they are all academic rather than real life applications. It is worthwhile to mention in this context however that two rough controllers have been implemented in hardware [4,13] displaying very attractive features. First of all their speed

42 citations

Book ChapterDOI
01 Jan 2004
TL;DR: The rough set processor (RSP) as mentioned in this paper is a simple processor based on elementary rough set granules and dependencies between them, which is meant to be used as an additional fast classification unit in ordinary computers or as an autonomous learning machine.
Abstract: In this chapter, the basics of the rough set approach are presented, and an outline of an exemplary processor structure is given. The organization of a simple processor is based on elementary rough set granules and dependencies between them. The rough set processor (RSP) is meant to be used as an additional fast classification unit in ordinary computers or as an autonomous learning machine. In the latter case, the RSP can be regarded as an alternative to neural networks.

41 citations


Cited by
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Book
08 Sep 2000
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Abstract: The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

23,600 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal ArticleDOI
TL;DR: The wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain and compares the wrapper approach to induction without feature subset selection and to Relief, a filter approach tofeature subset selection.

8,610 citations

Journal ArticleDOI
TL;DR: This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
Abstract: Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.

7,185 citations

01 Jan 1998
TL;DR: This thesis addresses the problem of feature selection for machine learning through a correlation based approach with CFS (Correlation based Feature Selection), an algorithm that couples this evaluation formula with an appropriate correlation measure and a heuristic search strategy.
Abstract: A central problem in machine learning is identifying a representative set of features from which to construct a classification model for a particular task. This thesis addresses the problem of feature selection for machine learning through a correlation based approach. The central hypothesis is that good feature sets contain features that are highly correlated with the class, yet uncorrelated with each other. A feature evaluation formula, based on ideas from test theory, provides an operational definition of this hypothesis. CFS (Correlation based Feature Selection) is an algorithm that couples this evaluation formula with an appropriate correlation measure and a heuristic search strategy. CFS was evaluated by experiments on artificial and natural datasets. Three machine learning algorithms were used: C4.5 (a decision tree learner), IB1 (an instance based learner), and naive Bayes. Experiments on artificial datasets showed that CFS quickly identifies and screens irrelevant, redundant, and noisy features, and identifies relevant features as long as their relevance does not strongly depend on other features. On natural domains, CFS typically eliminated well over half the features. In most cases, classification accuracy using the reduced feature set equaled or bettered accuracy using the complete feature set. Feature selection degraded machine learning performance in cases where some features were eliminated which were highly predictive of very small areas of the instance space. Further experiments compared CFS with a wrapper—a well known approach to feature selection that employs the target learning algorithm to evaluate feature sets. In many cases CFS gave comparable results to the wrapper, and in general, outperformed the wrapper on small datasets. CFS executes many times faster than the wrapper, which allows it to scale to larger datasets. Two methods of extending CFS to handle feature interaction are presented and experimentally evaluated. The first considers pairs of features and the second incorporates iii feature weights calculated by the RELIEF algorithm. Experiments on artificial domains showed that both methods were able to identify interacting features. On natural domains, the pairwise method gave more reliable results than using weights provided by RELIEF.

3,533 citations