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Rough Sets Turn 40: From Information Systems to Intelligent Systems

Skowron Andrzej, Ślęzak Dominik

Annals of Computer Science and Information Systems

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2022

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Vol. 30

23--34

EN

The theory of rough sets was founded by Zdzisław Pawlak to serve as a framework for data and knowledge exploration. Following Professor Pawlak's seminal paper titled ``Rough Sets'' published in 1982 in International Journal of Computer and Information Sciences, it is important to discuss the history, the presence and possible future developments of this theory, as well as its applications. One of the key aspects that lets us use rough sets in practical scenarios is the notion of information system, which in fact comes from even earlier Professor Pawlak's works. Information systems are the means for data and knowledge representation. They constitute the input to rough set mechanisms aimed at computing concept approximations and deriving compacted and interpretable decision models. Accordingly, in this paper we discuss where information systems come from. We claim that in many applications it is not enough to treat a data set -- represented as an information system -- as a purely mathematical object with no linkage to the data origins. Quite oppositely, in practice we may need to work with information systems more actively, giving ourselves a technical possibility to construct them dynamically, taking into account interaction with physical environments where the data is created.

2

KnowledgePit Meets BrightBox: A Step Toward Insightful Investigation of the Results of Data Science Competitions

Janusz Andrzej, Ślęzak Dominik

Annals of Computer Science and Information Systems

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2022

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Vol. 30

393--398

EN

We discuss the benefits of integrating the KnowledgePit data mining competition platform with the BrightBox technology aimed at diagnostics of machine learning models. We also show how to combine solutions submitted by the competition participants in order to obtain more accurate predictions.

3

Heuristic search of exact biclusters in binary data

Michalak Marcin, Jaksik Roman, Ślęzak Dominik

International Journal of Applied Mathematics and Computer Science

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2020

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Vol. 30, no. 1

161--171

EN

The biclustering of two-dimensional homogeneous data consists in finding a subset of rows and a subset of columns whose intersection provides a set of cells whose values fulfil a specified condition. Usually it is defined as equality or comparability. One of the presented approaches is based on the model of Boolean reasoning, in which finding biclusters in binary or discrete data comes down to the problem of finding prime implicants of some Boolean function. Due to the high computational complexity of this task, the application of some heuristics should be considered. In the paper, a modification of the well-known Johnson strategy for prime implicant approximation induction is presented, which is necessary for the biclustering problem. The new method is applied to artificial and biomedical datasets.

4

Network Device Workload Prediction: A Data Mining Challenge at Knowledge Pit

Janusz Andrzej, Przyborowski Mateusz, Biczyk Piotr, Ślęzak Dominik

Annals of Computer Science and Information Systems

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2020

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Vol. 21

77--80

EN

FedCSIS 2020 Data Mining Challenge: Network Device Workload Prediction was the seventh edition of the international data mining competition organized at Knowledge Pit, in association with the Conference on Computer Science and Information Systems. The main goal was to answer the question of whether it is possible to reliably predict workload-related characteristics of monitored network devices based on historical readings. We describe the scope and explain the motivation for this challenge. We also analyze solutions uploaded by the most successful participants and investigate prediction errors which had the greatest influence on the results. Finally, we describe our baseline solution to the considered problem, which turned out to be the most reliable in the final evaluation.

5

Introducing LogDL - Log Description Language for Insights from Complex Data

Świechowski Maciej, Ślęzak Dominik

Annals of Computer Science and Information Systems

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2020

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Vol. 21

145--154

EN

We propose a new logic-based language called LogDL (Log Description Language) that is designed to be a medium for the knowledge discovery workflows conducted over multimodal process-related and spatio-temporal data sets. It makes it possible to operate with the original data along with machine-learning-driven insights expressed as facts, rules and formulas, regarded as higher-level descriptive logs reflecting knowledge about the observed processes in real or virtual environments. LogDL is inspired by the research at the border of AI and games, precisely by GDL (Game Description Language) that was developed for General Game Playing. We compare LogDL to GDL, emphasizing that formal frameworks for analyzing gameplay data sets are a good prerequisite for the case of real,``not digital'' processes. As LogDL is a logic-based language, we present its syntax and semantics. We also discuss how to design its high-performance interpreter that is a must for commercial scenarios.

6

On Boolean Representation of Continuous Data Biclustering

Michalak Marcin, Ślęzak Dominik

Fundamenta Informaticae

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2019

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Vol. 167, nr 3

193--217

EN

Biclustering is considered as the method of finding two-dimensional subgroups in a matrix of scalars. The paper introduces a new approach to biclustering continuous matrices on the basis of boolean function analysis. We draw the strong relation between inclusion-maximal (maximal with respect to inclusion) biclusters of the assumed maximal difference between the data in a bicluster and prime implicants of a boolean function describing the data. These biclusters are called similarity biclusters. In the opposition to them, a new notion of dissimilarity biclusters was also introduced in the paper.