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Constructing decision quivers
P. 69–80.
Rule Learning and Formal Concept Analysis (FCA) are two fields of science that study similar topic yet speak in a very different terms. This paper describes rule-based machine learning models with FCA-based terminology which results in decision quiver model. A decision quiver, discussed in the paper, is a supervised machine learning model that is based on intents, generators of intents, and predictions for each intent (or generator). We show that the finding of the optimal set of intents is a cornerstone task in constructing a decision quiver (and thus, any rule-based model). The paper finishes with the baseline algorithm to construct decision quivers. The algorithm produces machine learning models that are much smaller than the state-of-the-art ensembles of decision trees, yet that offer the similar quality of predictions.
In book
Vol. 3489. , CEUR-WS.org, 2023.
Avdoshin S. M., Pesotskaya E. Y., Информационные технологии 2026 Т. 32 № 4 С. 185–194
With the rapid advancement of artificial intelligence, and deep learning in particular, models have emerged that are capable of delivering highly accurate predictions. However, the internal logic of such models remains difficult to interpret—an issue of critical importance, especially in domains where the correctness of an algorithm directly affects high-stakes decision-making. One promising avenue for ...
Added: May 8, 2026
Avdoshin S. M., Pesotskaya E. Y., Business Informatics 2026 Vol. 20 No. 1 P. 7–28
The rapid development of artificial intelligence (AI) is accompanied by increasing computational
complexity and decreasing model transparency, which significantly limits its adoption in critical
domains that require a high level of trust, interpretability, and justification of decisions. Under these
conditions, the field of Explainable Artificial Intelligence (XAI) has gained particular importance as it
focuses on approaches and technologies that ...
Added: May 8, 2026
D.D. Sukhoverkhova, L.N. Shchur, , in: Параллельные вычислительные технологии – XIX всероссийская конференция с международным участием, ПаВТ'2025. Короткие статьи и описания плакатов.: Издательский центр ЮУрГУ, 2025. P. 82–89.
We apply supervised deep machine learning techniques to extract properties of the anisotropic Ising model. We consider two cases of anisotropy: orthogonal and diagonal. From the predictions of the neural network, we obtained phase probability functions, from which we measured two quantities: the critical temperature and the critical exponent of the correlation length. We estimated ...
Added: December 4, 2025
Junyu B., Fei H., Huilin F. et al., International Journal of Approximate Reasoning 2025 Vol. 187 Article 109541
In Formal Concept Analysis (FCA), concept reduction serves as an important means of simplification. The application scenarios of concept reduction cover various aspects such as data mining, knowledge discovery, strategic decision-making, and rule learning. For symmetric formal contexts, a specialized class of concept reduction exists that can fully recover all knowledge. However, most existing concept ...
Added: December 1, 2025
Chertenkov V., Shchur L., Physical Review E - Statistical, Nonlinear, and Soft Matter Physics 2025 Vol. 112 No. 3 Article 034104
The main question raised in the article is whether a neural network trained on a spin lattice model in one universality class can be used to test a model in another universality class. The quantities of interest are the critical phase transition temperature and the correlation length exponent. In other words, the question of ...
Added: August 12, 2025
Dudyrev E., Mariia Zueva, Kuznetsov S. et al., , in: FCA4AI 2024: The 12th International Workshop "What can FCA do for Artificial Intelligence?", October 19 2024, Santiago de Compostela, SpainVol. 3911.: CEUR Workshop Proceedings, 2024. P. 47–58.
Clustering aims at finding disjoint groups of similar objects in data and is one major task in Machine Learning. It is also gaining more attention in Formal Concept Analysis community in these last years. This paper proposes an original approach to the clustering of complex data based on Formal Concept Analysis (FCA) and Pattern Structures. ...
Added: April 30, 2025
CEUR Workshop Proceedings, 2024.
The eleven preceding editions of the FCA4AI Workshop showed that many researchers working in Articial Intelligence are deeply interested in a well-founded method for classication and data mining such as Formal Concept Analysis (see https://upriss.github.io/fca/fca.html).
The FCA4AI Workshop Series started with ECAI 2012 (Montpellier) and the last edition was co-located with IJCAI 2023 (Macao, China). The ...
Added: April 29, 2025
D. D. Sukhoverkhova, L. N. Shchur, Lobachevskii Journal of Mathematics 2025 Vol. 46 No. 1 P. 528–534
We investigate the possibility of extracting features of second-order phase transitions using transfer machine learning. We have performed supervised machine learning for binary classification of snapshots of the spin distribution of the isotropic Ising model. The binary classification is performed in ferromagnetic and paramagnetic phases using a known critical temperature. The trained network is used ...
Added: January 13, 2025
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
Shalileh S., Koptseva A., Shishkovskaya T. et al., Доклады Российской академии наук. Математика, информатика, процессы управления (ранее - Доклады Академии Наук. Математика) 2023 Т. 514 № 2 С. 242–249
This paper represents our research to (i) propose an artificial intelligence, AI-based solution to identify depression and (ii) investigate our psychiatric knowledge. Concerning the first objective, we collected and annotated a new audio data set, and scrutinized the performance of eight regression approaches. Our studies showed that k-nearest neighbor and random forest form the group ...
Added: February 2, 2024
Baklanova V., Kurkin A., Teplova T., China Finance Review International 2024 Vol. 14 No. 3 P. 522–548
Purpose – The primary objective of this research is to provide a precise interpretation of the constructed
machine learning model and produce definitive summaries that can evaluate the influence of investor sentiment on the overall sales of non-fungible token (NFT) assets. To achieve this objective, the NFT hype
index was constructed as well as several approaches of ...
Added: December 10, 2023
Dudyrev E., Kuznetsov S., Napoli A., , in: 17th International Conference, ICFCA 2023, Kassel, Germany, July 17–21, 2023, Proceedings. Formal Concept Analysis, (LNCS, volume 13934).: Switzerland: Springer, 2023. P. 127–142.
In this paper we introduce and study description quivers as compact representations of concept lattices and respective ensembles of decision trees. Formally, description quivers are directed multigraphs where vertices represent concept intents and (multiple) edges represent generators of intents. We study some properties of description quivers and shed light on their use for describing state-of-the-art symbolic machine ...
Added: October 4, 2023
Parakal E. G., Kuznetsov S., , in: Proceedings of the 10th International Workshop "What can FCA do for Artificial Intelligence?"Vol. 3233.: CEUR Workshop Proceedings, 2022. Ch. 2 P. 9–22.
Explanations for the predictions made by Machine Learning (ML) models are best framed in terms of
abstract, high-level concepts that are easily comprehensible to human beings. The use of such concepts
constitutes a subfield of interpretability methods known as concept-based explanations. This work uses
concept-based explanations to build an intrinsically interpretable document classifier using a combination
of Formal Concept ...
Added: May 17, 2023
Egurnov D., Точилкин Д. С., Ignatov D. I., , in: Complex Data Analytics with Formal Concept Analysis.: Springer, 2022. P. 239–258.
In this paper, we describe versions of triclustering algorithms adapted for efficient calculations in distributed environments with MapReduce model or parallelisation mechanism provided by modern programming languages. OAC-family of triclustering algorithms shows good parallelisation capabilities due to the independent processing of triples of a triadic formal context. We provide time and space complexity of the ...
Added: November 1, 2022
Springer, 2022.
FCA is an important formalism that is associated with a variety of research areas such as lattice theory, knowledge representation, data mining, machine learning, and semantic Web. It is successfully exploited in an increasing number of application domains such as software engineering, information retrieval, social network analysis, and bioinformatics. Its mathematical power comes from its concept ...
Added: November 1, 2022
Galitsky B., Ilvovsky D., Goncharova E., , in: Proceedings of the 10th International Workshop "What can FCA do for Artificial Intelligence?"Vol. 3233.: CEUR Workshop Proceedings, 2022. P. 75–87.
Supported decision trees that have been first proposed to boost the performance and the explainability of the expert systems built upon the texts can become a great basis for the machine reading comprehension (MRC) systems. The supported decision tree is based on building and combining the corresponding discourse trees for the text passage. In this work, ...
Added: November 1, 2022
Dudyrev E., Kuznetsov S., , in: Proceedings of the 10th International Workshop "What can FCA do for Artificial Intelligence?"Vol. 3233.: CEUR Workshop Proceedings, 2022. P. 23–34.
Studies on Explainable Artificial Intelligence show that a model should be small in order to be human understandable. The restriction on the size of a model drastically reduces the space of possible solutions. Many rule learning models still rely on greedy algorithms for generating ensembles of decision trees. This paper discusses FCA-inspired mathematical and engineering ...
Added: November 1, 2022
Suvorova A., , in: Digital Transformation and Global Society. 6th International Conference, DTGS 2021, St. Petersburg, Russia, June 23–25, 2021, Revised Selected Papers.: Springer, 2022. P. 319–331.
The increasing use of intelligent technologies, the development and implementation of machine learning systems in various spheres of life require explaining machine learning-based decisions in such systems. This need for interpretation leads to the increasing development of new methods for interpreting machine learning models and their more intense use in real systems. The paper reviews ...
Added: September 28, 2022