• A
  • A
  • A
  • АБВ
  • АБВ
  • АБВ
  • A
  • A
  • A
  • A
  • A
Обычная версия сайта
  • RU
  • EN
  • HSE University
  • Publications
  • Book chapter
  • Ensemble Techniques for Lazy Classification Based on Pattern Structures
  • RU
  • EN
Расширенный поиск
Высшая школа экономики
Национальный исследовательский университет
Priority areas
  • business informatics
  • economics
  • engineering science
  • humanitarian
  • IT and mathematics
  • law
  • management
  • mathematics
  • sociology
  • state and public administration
by year
  • 2028
  • 2027
  • 2026
  • 2025
  • 2024
  • 2023
  • 2022
  • 2021
  • 2020
  • 2019
  • 2018
  • 2017
  • 2016
  • 2015
  • 2014
  • 2013
  • 2012
  • 2011
  • 2010
  • 2009
  • 2008
  • 2007
  • 2006
  • 2005
  • 2004
  • 2003
  • 2002
  • 2001
  • 2000
  • 1999
  • 1998
  • 1997
  • 1996
  • 1995
  • 1994
  • 1993
  • 1992
  • 1991
  • 1990
  • 1989
  • 1988
  • 1987
  • 1986
  • 1985
  • 1984
  • 1983
  • 1982
  • 1981
  • 1980
  • 1979
  • 1978
  • 1977
  • 1976
  • 1975
  • 1974
  • 1973
  • 1972
  • 1971
  • 1970
  • 1969
  • 1968
  • 1967
  • 1966
  • 1965
  • 1964
  • 1963
  • 1958
  • More
Subject
News
September 25, 2026
AI Users Earn Up to 41.8% More Than Non-Users
Research conducted by economists at HSE University has revealed a significant correlation between the regular use of GenAI in the workplace and higher pay among Russian employees. The study found that individuals who frequently use GenAI in their professional activities earn notably more than those who reject these new tools or resort to them occasionally. The salary premium for highly qualified specialists reaches 41.8%. The article was published in the Voprosy Ekonomiki journal.
September 24, 2026
‘Feedback and Constructive Criticism Are Essential in Our Profession
Vincent Fardeau, Associate Professor at HSE ICEF, has reached a major career milestone: he recently published his paper ‘Asymmetric Thin Markets’ in the Journal of Financial Economics, successfully passed his major academic review, and received tenure. In this interview, Vincent discusses the story behind the paper, explains the concept of asymmetric thin markets, and shares his advice for young scholars aiming to publish in top-tier journals.
September 22, 2026
Personal Interest in Doctoral Thesis Topic Most Important for Confidence in Successful Defence
A researcher at HSE University analysed data on 1,539 doctoral students from 161 Russian universities to identify which features of a thesis topic are associated with academic success and engagement. The most important factor was found to be personal interest in the research topic, which was associated with almost all key aspects of doctoral programme experience—from engaging with the academic supervisor to research activity and confidence about successfully defending the thesis. The findings have been published in Higher Education.

 

Have you spotted a typo?
Highlight it, click Ctrl+Enter and send us a message. Thank you for your help!

Publications
  • Books
  • Articles
  • Chapters of books
  • Working papers
  • Report a publication
  • Research at HSE

?

Ensemble Techniques for Lazy Classification Based on Pattern Structures

P. 105–112.
Ilya Semenkov, Sergei O. Kuznetsov

This paper presents different versions of classification ensemble methods based on pattern structures. Each of these methods is described and tested on multiple datasets (including datasets with exclusively numerical and exclusively nominal features). As a baseline model Random Forest generation is used. For some classification tasks the classification algorithms based on pattern structures showed better performance than Random Forest. The quality of the algorithms is noticeably dependent on ensemble aggregation function and on boosting weighting scheme.

Language: English
Full text
Text on another site
Keywords: анализ формальных понятийpattern structuresузорные структурыEnsemble algorithmsBoosting AlgorithmsFormal Concept Analysis (FCA)алгоритмы бустингаансамблевые алгоритмы

In book

Proceedings of the 9th International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2021)
Vol. 2972. , CEUR-WS, 2021.
Similar publications
Atomic Patterns for Efficient Computation with Pattern Structures
Dudyrev E., Couceiro M., Kaytoue M. et al., , in: Second International Joint Conference, CONCEPTS 2025, Cluj-Napoca, Romania, September 8–12, 2025, Proceedings. Conceptual Knowledge Structures. LNCS, volume 15941.: Cham: Springer, 2025. P. 178–194.
Pattern Structures is a framework in FCA allowing objects to have complex descriptions, only requiring that the set of descriptions forms a complete meet-semi-lattice. However, some particular descrip tions or patterns, such as subgraphs and subsequences, do not necessarily ensure that every pair of descriptions has a unique infimum and ask for additional operations, e.g., ...
Added: December 1, 2025
Traffic Prediction Based on Formal Concept-Enhanced Federated Graph Learning
Wu K., Hao F., Yao R. et al., IEEE Transactions on Intelligent Transportation Systems 2025 Vol. 26 No. 5 P. 6936–6948
Aiming to improve the efficiency of urban traffic management, previous studies have achieved considerable traffic prediction accuracy. For example, methods based on time series analysis perform well in short-term traffic prediction, and neural networks show strong capabilities in processing complex nonlinear relationships within traffic data. However, previous studies also have the following two limitations: 1) ...
Added: December 1, 2025
Binary relations-preserving incremental pseudo-equiconcept reduction for symmetric formal context
Huilin F., Fei H., Linkai Z. et al., Expert Systems with Applications 2025 Vol. 276 Article 127086
Concept reduct refers to the minimal subset of concepts that preserves the binary relation of the binary data table (formal context). Importantly, it reduces the complexity of problem-solving and improves the efficiency of concept-cognition using formal concept analysis (FCA). Particularly, for a symmetric formal context, there exists a significant class of concept reducts given by ...
Added: December 1, 2025
Explainable Document Classification via Concept Whitening and Stable Graph Patterns
Parakal E. G., Kuznetsov S., Makarov I. et al., IEEE Access 2025 Vol. 13 P. 149657–149678
This paper proposes a novel explainable document classification framework that integrates Concept Whitening (CW) with graph concepts that are derived from stable graph patterns, and extracted via methods based on Formal Concept Analysis (FCA) and pattern structures. Document graphs are constructed using Abstract Meaning Representation (AMR) graphs, from which graph concepts are extracted and aligned ...
Added: October 22, 2025
Explainable Document Classification via Pattern Structures
Sergei O. Kuznetsov, Parakal E. G., Lecture Notes in Networks and Systems 2023 Vol. 776 P. 423–434
Inherently explainable Machine Learning (ML) models are able to provide explanations for their predictions by virtue of their construction. The explanations of a ML model are more comprehensible if they are expressed in terms of its input features. Our paper proposes an inherently explainable pipeline for document classification using pattern structures and Abstract Meaning Representation ...
Added: February 5, 2024
On the Number of Maximal Antichains in Boolean Lattices for 𝑛 up to 7
Ignatov D. I., Lobachevskii Journal of Mathematics 2023 No. 44 P. 137–146
We consider two ways how to compute the number of maximal antichains in the Boolean lattice on 𝑛 elements. The first one is based on full direct enumeration, while the second ones relies on concept lattices or Galois lattices (studied in Formal Concept Analysis, an applied branch of lattice theory) and the Dedekind–MacNeille completion of a partial ...
Added: June 13, 2023
АНАЛИЗ СТРУКТУРЫ ВРЕМЕННЫХ РЯДОВ КОЛИЧЕСТВА ДЕЛ В СУДЕ
Lukianchenko P., Gromov V., Beschastnov Y. et al., Вестник кибернетики 2022 Т. 4 № 48 С. 37–48
The study analyzes the time series of the number of new cases in the administrative courts of the Russian Federation using two methods of time series grouping according to the chaotic, stochastic, and regular structure. The first model is based on the entropy‒complexity plane, the second one is presented by the attribute‒object graph. As a result, four groups ...
Added: March 20, 2023
Применение методов анализа формальных понятий для анализа временных рядов тока крови для гемодиализных больных
Gromov V., Урманцева Н. Р., [б.и.], 2021.
В докладе рассматриваются подходы к прогнозированию на основе кластеризации, опирающиеся на методологию анализа формальных понятий. Методология применяется для кластеризации участков временного ряда с целью выделения характерных участков (мотивов), отвечающих больным с различной степенью засорённости фистулы. ...
Added: January 30, 2023
Summation of Decision Trees
Dudyrev E., Kuznetsov S., , in: Proceedings of the 9th International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2021)Vol. 2972.: CEUR-WS, 2021. Ch. 9 P. 99–104.
Ensembles of decision trees, like Random Forests are efficient machine learning models with state-of-the-art prediction quality. However, their predictions are much less transparent than those of a single decision tree. In this paper, we describe a prediction model based on a single decision tree in terms of Formal Concept Analysis. We define a differential way ...
Added: December 8, 2021
Exploring the dataset structure by means of delta-classes of equivalence. The case of the titanic dataset?
Buzmakov A. V., Kuznetsov S., Makhalova T. et al., , in: Proceedings of the 9th International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2021)Vol. 2972.: CEUR-WS, 2021. Ch. 2 P. 19–26.
Added: December 7, 2021
Pattern Structures for Knowledge Processing and Information Retrieval
Kuznetsov S., Goncharova E., , in: Proceedings of the Fifth International Scientific Conference "Intelligent Information Technologies for Industry" (IITI'21)Vol. 330.: Springer, 2022. P. 410–420.
Added: October 28, 2021
Concept-based chatbot for interactive query refinement in product search
Goncharova E., Ilvovsky D., Galitsky B., , in: Proceedings of the 9th International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2021)Vol. 2972.: CEUR-WS, 2021. P. 51–58.
Added: October 28, 2021
Towards Stable Significant Subgroup Discovery
Jyoti -., Buzmakov Aleksey, Kailasam S., , in: The 15th International Conference on Concept Lattices and Their Applications CLA2020Issue 2668.: CEUR-WS, 2020. P. 287–292.
Added: July 10, 2021
Formal Concept Analysis: 16th International Conference, ICFCA 2021, Strasbourg, France, June 29 – July 2, 2021, Proceedings
Springer, 2021.
This book constitutes the proceedings of the 16th International Conference on Formal Concept Analysis, ICFCA 2021, held in Strasbourg, France, in June/July 2021. The 14 full papers and 5 short papers presented in this volume were carefully reviewed and selected from 32 submissions. The book also contains four invited contributions in full paper length. The research part ...
Added: July 10, 2021
On pattern setups and pattern multistructures
Belfodil A., Kuznetsov S., Kaytoue M., International Journal of General Systems 2020 Vol. 49 No. 8 P. 785–818
Order and lattice theory provides convenient mathematical tools for pattern mining, in particular for condensed irredundant representations of pattern spaces and their efficient generation. Formal Concept Analysis (FCA) offers a generic framework, called pattern structures, to formalize many types of patterns, such as itemsets, intervals, graphs, and sequence sets. Moreover, FCA provides generic algorithms to generate irredundantly all ...
Added: January 25, 2021
Proceedings of the Fifthteenth International Conference on Concept Lattices and Their Applications
CEUR-WS.org, 2020.
The CLA conference is an international forum for researchers, practitioners and students dedicated to the practice of Formal Concept Analysis (FCA) and areas closely related to it, including data analysis and mining, information retrieval, knowledge management, knowledge engineering, logic, algebra and lattice theory. The 15th of CLA, CLA 2020, was going to be held in Tallinn, Estonia ...
Added: October 30, 2020
Next Priority Concept: A new and generic algorithm computing concepts from complex and heterogeneous data
Kuznetsov S., Demko C., Bertet K. et al., , in: Electronic Procedings Theoretical Computer ScienceVol. 845.: [б.и.], 2020. P. 1–20.
In this article, we present a new data type agnostic algorithm calculating a concept lattice from heterogeneous and complex data. Our NextPriorityConcept algorithm is first introduced and proved in the binary case as an extension of Bordat's algorithm with the notion of strategies to select only some predecessors of each concept, avoiding the generation of ...
Added: October 29, 2020
Electronic Procedings Theoretical Computer Science
[б.и.], 2020.
Theoretical Computer Science is mathematical and abstract in spirit, but it derives its motivation from practical and everyday computation. Its aim is to understand the nature of computation and, as a consequence of this understanding, provide more efficient methodologies. All papers introducing or studying mathematical, logic and formal concepts and methods are welcome, provided that their motivation is ...
Added: October 29, 2020
  • About
  • About
  • Key Figures & Facts
  • Sustainability at HSE University
  • Faculties & Departments
  • International Partnerships
  • Faculty & Staff
  • HSE Buildings
  • HSE University for Persons with Disabilities
  • Public Enquiries
  • Studies
  • Admissions
  • Programme Catalogue
  • Undergraduate
  • Graduate
  • Exchange Programmes
  • Summer University
  • Summer Schools
  • Semester in Moscow
  • Business Internship
  • Research
  • International Laboratories
  • Research Centres
  • Research Projects
  • Monitoring Studies
  • Conferences & Seminars
  • Academic Jobs
  • Yasin (April) International Academic Conference on Economic and Social Development
  • Media & Resources
  • Publications by staff
  • HSE Journals
  • Publishing House
  • iq.hse.ru: commentary by HSE experts
  • Library
  • Economic & Social Data Archive
  • Video
  • HSE Repository of Socio-Economic Information
  • HSE1993–2026
  • Contacts
  • Copyright
  • Privacy Policy
  • Site Map
Edit