Beata Zielosko – författare

The two main goals of this book are (i) to provide tools for the experimental and theoretical study of decision trees with hypotheses and (ii) to compare these decision trees with conventional decision trees that use only queries, each based on a single attribute.

In this book, the concept of a hypothesis about the values of all attributes is added to the standard decision tree model, considered, in particular, in test theory and rough set theory. This extension allows us to use the analog of equivalence queries from exact learning and explore decision trees that are based on various combinations of attributes, hypotheses, and proper hypotheses (analog of proper equivalence queries). The two main goals of this book are (i) to provide tools for the experimental and theoretical study of decision trees with hypotheses and (ii) to compare these decision trees with conventional decision trees that use only queries, each based on a single attribute. Both experimental and theoretical results show that decision trees with hypotheses can have less complexity than conventional decision trees. These results open up some prospects for using decision trees with hypotheses as a means of knowledge representation and algorithms for computing Boolean functions. The obtained theoretical results and tools for studying decision trees with hypotheses are useful for researchers using decision trees and rules in data analysis. This book can also be used as the basis for graduate courses.

This book is a collection of papers related to the latest achievements and development trends in the field of artificial intelligence. This compilation of peer-reviewed articles presents both theoretical foundations and innovative algorithms, as well as interdisciplinary applications that shape modern intelligent technologies. It serves as a platform for critical academic discussion, integrating methodological foundations with the analysis of contemporary AI issues. This book points to many advances topically subdivided into parts related to data mining and machine learning, medical, interdisciplinary and human behaviour applications, neural network and deep learning systems, computer vision, uncertainty in AI, robotics and autonomous systems, problem solving and optimisation, generative AI, decision-making and classification in complex systems, and research of young scientist in field of AI. By addressing innovative approaches, implementation efficiency, and responsibility in applications, this book is a valuable source of knowledge for researchers, practitioners, and students in technical disciplines.

This two-volume set LNAI 10313 and LNAI 10314 constitutes the proceedings of the International Joint Conference on Rough Sets, IJCRS 2017, held in Olsztyn, Poland, in July 2017.The 74 revised full papers presented together with 16 short papers and 16 invited talks, were carefully reviewed and selected from 130 submissions. The papers in this two set-volume of IJCRS 2017 follow the track already rutted by RSCTC and JRS conferences which aimed at unification of many facets of rough set theory from theoretical aspects of the rough set idea bordering on theory of concepts and going through algebraic structures, topological structures, logics for uncertain reasoning, decision algorithms, relations to other theories of vagueness and ambiguity, then to extensions of the rough set idea like granular structures, rough mereology, and to applications of the idea in diverse fields of applied science including hybrid methods like rough-fuzzy, neuro-rough, neuro-rough-fuzzy computing. IJCRS 2017 encompasses topics spread among four main tracks: Rough Sets and Data Science (in relation to RSCTC series organized since 1998); Rough Sets and Granular Computing (in relation to RSFDGrC series organized since 1999); Rough Sets and Knowledge Technology (in relation to RSKT series organized since 2006); and Rough Sets and Intelligent Systems (in relation to RSEISP series organized since 2007).

This two-volume set LNAI 10313 and LNAI 10314 constitutes the proceedings of the International Joint Conference on Rough Sets, IJCRS 2017, held in Olsztyn, Poland, in July 2017. The 74 revised full papers presented together with 16 short papers and 16 invited talks, were carefully reviewed and selected from 130 submissions. The papers in this two set-volume of IJCRS 2017 follow the track already rutted by RSCTC and JRS conferences which aimed at unification of many facets of rough set theory from theoretical aspects of the rough set idea bordering on theory of concepts and going through algebraic structures, topological structures, logics for uncertain reasoning, decision algorithms, relations to other theories of vagueness and ambiguity, then to extensions of the rough set idea like granular structures, rough mereology, and to applications of the idea in diverse fields of applied science including hybrid methods like rough-fuzzy, neuro-rough, neuro-rough-fuzzy computing. IJCRS 2017encompasses topics spread among four main tracks: Rough Sets and Data Science (in relation to RSCTC series organized since 1998); Rough Sets and Granular Computing (in relation to RSFDGrC series organized since 1999); Rough Sets and Knowledge Technology (in relation to RSKT series organized since 2006); and Rough Sets and Intelligent Systems (in relation to RSEISP series organized since 2007).

This book presents recent developments and research trends in the field of feature selection for data and pattern recognition, highlighting a number of latest advances.The field of feature selection is evolving constantly, providing numerous new algorithms, new solutions, and new applications. Some of the advances presented focus on theoretical approaches, introducing novel propositions highlighting and discussing properties of objects, and analysing the intricacies of processes and bounds on computational complexity, while others are dedicated to the specific requirements of application domains or the particularities of tasks waiting to be solved or improved.Divided into four parts – nature and representation of data; ranking and exploration of features; image, shape, motion, and audio detection and recognition; decision support systems, it is of great interest to a large section of researchers including students, professorsand practitioners.

This book presents recent developments and research trends in the field of feature selection for data and pattern recognition, highlighting a number of latest advances. The field of feature selection is evolving constantly, providing numerous new algorithms, new solutions, and new applications.

This monograph is devoted to theoretical and experimental study of partial reductsandpartialdecisionrulesonthebasisofthestudyofpartialcovers. The use of partial (approximate) reducts and decision rules instead of exact ones allowsustoobtainmorecompactdescriptionofknowledgecontainedindecision tables,andtodesignmorepreciseclassi?ers. Weconsideralgorithmsforconstructionofpartialreductsandpartialdecision rules,boundsonminimalcomplexityofpartialreductsanddecisionrules,and algorithms for construction of the set of all partial reducts and the set of all irreducible partial decision rules. We discuss results of numerous experiments with randomly generated and real-life decision tables. These results show that partial reducts and decision rules can be used in data mining and knowledge discoverybothforknowledgerepresentationandforprediction. Theresultsobtainedinthe monographcanbe usefulforresearchersinsuch areasasmachinelearning,dataminingandknowledgediscovery,especiallyfor thosewhoareworkinginroughsettheory,testtheoryandLAD(LogicalAnalysis ofData). The monographcan be usedunder the creationofcoursesforgraduates- dentsandforPh. D. studies.An essential part of software used in experiments will be accessible soon in RSES-RoughSetExplorationSystem(InstituteofMathematics,WarsawU- versity,headofproject-ProfessorAndrzejSkowron). We are greatly indebted to Professor Andrzej Skowron for stimulated d- cussionsand varioussupportof ourwork. We aregratefulto ProfessorJanusz Kacprzykforhelpfulsuggestions. Sosnowiec,Poland MikhailJu. Moshkov April2008 MarcinPiliszczuk BeataZielosko Contents Introduction...1 1 PartialCovers,ReductsandDecisionRules ...7 1. 1 PartialCovers...8 1. 1. 1 MainNotions...8 1. 1. 2 Known Results...9 1. 1. 3 PolynomialApproximateAlgorithms...10 1. 1. 4 Bounds on C (?)Based on Information about min GreedyAlgorithm Work...13 1. 1. 5 UpperBoundon C (?)...17 greedy 1. 1. 6 Covers fortheMostPartofSetCoverProblems...18 1. 2 PartialTests and Reducts...22 1. 2. 1 MainNotions...22 1. 2. 2Relationships betweenPartialCovers and Partial Tests...23 1. 2. 3 PrecisionofGreedyAlgorithm...24 1. 2. 4 PolynomialApproximateAlgorithms...25 1. 2. 5 Bounds on R (?)Based on Information about min GreedyAlgorithm Work...26 1. 2. 6 UpperBoundon R (?)...28 greedy 1. 2. 7 Tests fortheMostPartofBinaryDecisionTables...29 1.3 PartialDecision Rules...

This monograph is devoted to theoretical and experimental study of partial reductsandpartialdecisionrulesonthebasisofthestudyofpartialcovers. The use of partial (approximate) reducts and decision rules instead of exact ones allowsustoobtainmorecompactdescriptionofknowledgecontainedindecision tables,andtodesignmorepreciseclassi?ers. Weconsideralgorithmsforconstructionofpartialreductsandpartialdecision rules,boundsonminimalcomplexityofpartialreductsanddecisionrules,and algorithms for construction of the set of all partial reducts and the set of all irreducible partial decision rules. We discuss results of numerous experiments with randomly generated and real-life decision tables. These results show that partial reducts and decision rules can be used in data mining and knowledge discoverybothforknowledgerepresentationandforprediction. Theresultsobtainedinthe monographcanbe usefulforresearchersinsuch areasasmachinelearning,dataminingandknowledgediscovery,especiallyfor thosewhoareworkinginroughsettheory,testtheoryandLAD(LogicalAnalysis ofData). The monographcan be usedunder the creationofcoursesforgraduates- dentsandforPh. D. studies.An essential part of software used in experiments will be accessible soon in RSES-RoughSetExplorationSystem(InstituteofMathematics,WarsawU- versity,headofproject-ProfessorAndrzejSkowron). We are greatly indebted to Professor Andrzej Skowron for stimulated d- cussionsand varioussupportof ourwork. We aregratefulto ProfessorJanusz Kacprzykforhelpfulsuggestions. Sosnowiec,Poland MikhailJu. Moshkov April2008 MarcinPiliszczuk BeataZielosko Contents Introduction...1 1 PartialCovers,ReductsandDecisionRules ...7 1. 1 PartialCovers...8 1. 1. 1 MainNotions...8 1. 1. 2 Known Results...9 1. 1. 3 PolynomialApproximateAlgorithms...10 1. 1. 4 Bounds on C (?)Based on Information about min GreedyAlgorithm Work...13 1. 1. 5 UpperBoundon C (?)...17 greedy 1. 1. 6 Covers fortheMostPartofSetCoverProblems...18 1. 2 PartialTests and Reducts...22 1. 2. 1 MainNotions...22 1. 2. 2Relationships betweenPartialCovers and Partial Tests...23 1. 2. 3 PrecisionofGreedyAlgorithm...24 1. 2. 4 PolynomialApproximateAlgorithms...25 1. 2. 5 Bounds on R (?)Based on Information about min GreedyAlgorithm Work...26 1. 2. 6 UpperBoundon R (?)...28 greedy 1. 2. 7 Tests fortheMostPartofBinaryDecisionTables...29 1.3 PartialDecision Rules...

Decision trees and decision rule systems are widely used in different applicationsas algorithms for problem solving, as predictors, and as a way forknowledge representation. Reducts play key role in the problem of attribute(feature) selection. The aims of this book are (i) the consideration of the setsof decision trees, rules and reducts; (ii) study of relationships among theseobjects; (iii) design of algorithms for construction of trees, rules and reducts;and (iv) obtaining bounds on their complexity. Applications for supervisedmachine learning, discrete optimization, analysis of acyclic programs, faultdiagnosis, and pattern recognition are considered also. This is a mixture ofresearch monograph and lecture notes. It contains many unpublished results.However, proofs are carefully selected to be understandable for students.The results considered in this book can be useful for researchers in machinelearning, data mining and knowledge discovery, especially for those who areworking in rough set theory, test theory and logical analysis of data. The bookcan be used in the creation of courses for graduate students.

Decision trees and decision rule systems are widely used in different applicationsas algorithms for problem solving, as predictors, and as a way forknowledge representation. Reducts play key role in the problem of attribute(feature) selection. The aims of this book are (i) the consideration of the setsof decision trees, rules and reducts; (ii) study of relationships among theseobjects; (iii) design of algorithms for construction of trees, rules and reducts;and (iv) obtaining bounds on their complexity. Applications for supervisedmachine learning, discrete optimization, analysis of acyclic programs, faultdiagnosis, and pattern recognition are considered also. This is a mixture ofresearch monograph and lecture notes. It contains many unpublished results.However, proofs are carefully selected to be understandable for students.The results considered in this book can be useful for researchers in machinelearning, data mining and knowledge discovery, especially for those who areworking in rough set theory, test theory and logical analysis of data. The bookcan be used in the creation of courses for graduate students.

In this book, the following three approaches to data analysis are presented:- Test Theory, founded by Sergei V. Yablonskii (1924-1998); the first publications appeared in 1955 and 1958,-           Rough Sets, founded by Zdzisław I. Pawlak (1926-2006); the first publications appeared in 1981 and 1982,-           Logical Analysis of Data, founded by Peter L. Hammer (1936-2006); the first publications appeared in 1986 and 1988.These three approaches have much in common, but researchers active in one of these areas often have a limited knowledge about the results and methods developed in the other two. On the other hand, each of the approaches shows some originality and we believe that the exchange of knowledge can stimulate further development of each of them. This can lead to new theoretical results and real-life applications and, in particular, new results based on combination of these three data analysis approaches can be expected.-           Logical Analysis of Data, founded by Peter L. Hammer (1936-2006); the firstpublications appeared in 1986 and 1988.These three approaches have much in common, but researchers active in one of these areas often have a limited knowledge about the results and methods developed in the other two. On the other hand, each of the approaches shows some originality and we believe that the exchange of knowledge can stimulate further development of each of them. This can lead to new theoretical results and real-life applications and, in particular, new results based on combination of these three data analysis approaches can be expected.These three approaches have much in common, but researchers active in one of these areas often have a limited knowledge about the results and methods developed in the other two. On the other hand, each of the approaches shows some originality and we believe that the exchange of knowledge can stimulate further development of each of them. This can lead to new theoretical results and real-life applications and, in particular, new results based on combination of these three data analysis approaches can be expected.