Wyniki wyszukiwania - Biblioteka Nauki

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Combination of Fuzzy Sets and Rough Sets for Machine Learning Purposes (Tutorial Lecture — Extended Abstract)

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Cornelis C. , Bollaert H.

Annals of Computer Science and Information Systems

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2023

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tom Vol. 35

67--67

EN

Fuzzy set theory is a popular AI tool designed to model and process vague information. Specifically, it is based on the idea that membership to a given concept, or logical truthhood of a given proposition, can be a matter of degree. On the other hand, rough set theory was proposed as a way to handle potentially inconsistent data inside information systems. In Pawlak's original proposal, this is achieved by providing a lower and upper approximation of a concept, using the equivalence classes of an indiscernibility relation as building blocks. Noting the highly complementary characteristics of fuzzy sets and rough sets, Dubois and Prade proposed the first working definition of a fuzzy rough set, and thus paved the way for a flourishing hybrid theory with numerous theoretical and practical advances. In this tutorial, we will explain how fuzzy rough sets may be successfully applied to a variety of machine learning problems. After a brief discussion of how the hybridization between fuzzy sets and rough sets may be achieved, including an extension based on ordered weighted average operators, we will focus on the following practical applications: 1. Fuzzy-rough nearest neighbor (FRNN) classification, along with its adaptations for imbalanced datasets and multi-label datasets 2. Fuzzy-rough feature selection (FRFS) 3. Fuzzy-rough instance selection (FRIS) and Fuzzy-rough prototype selection (FRPS) We will also demonstrate software implementations of all of these algorithms in the Python library fuzzy-rough-learn.

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I manuali di italiano per autodidatti nella Polonia del primo Novecento – un caso esemplare: "Praktyczna metoda języka włoskiego" di F. Giannini e C. Moscheni

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Jamrozik E.

Annales Universitatis Paedagogicae Cracoviensis. Studia de Cultura

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2017

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nr 9(3)

194-207

IT

Sullo sfondo delle caratteristiche generali dell’insegnamento delle lingue straniere in Polonia all’inizio del Novecento viene presentato il manuale di italiano Praktyczna metoda języka włoskiego di Fortunato Giannini e Carlo Moscheni. Si passano in rassegna la struttura del manuale e le varie metodologie che gli autori applicano nel corso dell’insegnamento al fine di mettere in risalto rispettivamente l’approccio contrastivo, filologico e pragmatico, nonché il valore delle informazioni sulla lingua e cultura italiana.

PL

Celem artykułu jest przedstawienie specyfiki nauczania języka włoskiego w Polsce w pierwszej połowie XX wieku. Na tle ogólnej charakterystyki nauczania języków obcych język włoski – nie objęty programem nauczania szkolnego po odzyskaniu niepodległości – jawi się jako język niszowy, dlatego też publikowane w omawianym okresie podręczniki przeznaczone są w dużej mierze do samodzielnej nauki. Jako reprezentatywny dla tej kategorii został omówiony podręcznik Praktyczna metoda języka włoskiego autorstwa F. Gianniniego i C. Moscheni. Ilustrowana przykładami analiza przedstawia sposób, w jaki łączy on aplikację metody gramatyczno-tłumaczeniowej z elementami metod bezpośrednich, nakierowanych na wykształcenie kompetencji komunikacyjnych, odpowiadając w ten sposób na oczekiwania użytkownika języka włoskiego w tym okresie historycznym.

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Rough Sets: Introduction, History and Selected Applications (Tutorial Lecture — Extended Abstract)

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Dutta S. , Ciucci D.

Annals of Computer Science and Information Systems

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2023

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tom Vol. 35

69--70

EN

The aim of this tutorial is to present a brief overview of the theory of rough sets from the perspective of its mathematical foundations, history of development, as well as connections with other branches of mathematics and informatics. The content concerns both the theoretical and practical aspects of applications. The above mentioned target of the tutorial will be covered in two parts. In the first part we would aim to present the introduction to rough sets and the second part will focus on the connections with other branches of mathematics and informatics. In particular, in the second part, we will discuss the connections of rough sets with logics, topology and algebra, and graph theory (when it comes to mathematics), as well as knowledge representation, machine learning and data mining, and theoretical computer science (when it comes to informatics).

4

Reducts in Rough Sets: Algorithmic Insights, Open Source Libraries and Applications (Tutorial – Extended Abstract)

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Janusz A. , Stawicki S.

Annals of Computer Science and Information Systems

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2023

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tom Vol. 35

71--71

EN

The theory of rough sets is a powerful mathematical framework for handling imprecise or uncertain information in data analysis and decision-making. At its core, rough set theory introduces the concept of decision reducts, which are subsets of attributes or features that preserve the essential information needed to make accurate decisions while eliminating redundant or irrelevant information. By identifying ensembles of decision reducts, analysts can simplify complex datasets, improve classification accuracy, and gain valuable insights from noisy or incomplete data. These appealing characteristics make rough sets a valuable tool in various fields, including machine learning, data mining, and expert systems. There have been proposed many extensions to the notion of decision reducts, such as approximate decision reducts, dynamic decision reducts, DAARs, decision bireducts, and many others. The key objective of most of them was to prevent the inclusion of illusionary dependencies between attributes and decision values to the reducts. A lot of research was also committed to the problem of algorithms for the efficient computation of diverse reduct sets. This topic is particularly important from the perspective of practical applications of the rough set theory. In this tutorial, we focus on the latter aspect of the decision reduct-related research. We discuss various, both, well-known and relatively new algorithms, and consider their specific advantages. We explain in detail selected implementation aspects that are crucial for the efficient computation of many types of decision reducts. We also overview and demonstrate libraries in popular programming languages that allow easy computation of reducts on real-world datasets, including RoughSets library for R and a novel Python language library scikit-rough. Finally, we share the results of a study aiming at the comparison of the computational efficiency of various reduct algorithms.