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May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
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?

A First Study on What MDL Can Do for FCA

.
Makhalova T., Napoli A., Kuznetsov S.
Language: English
Keywords: MDL principle Formal Concept AnalysisInterestingness measures
Publication based on the results of:
Well-interpretable Methods of Knowledge Discovery and Knowledge Representation (2018)

In book

CLA 2018: The 14th International Conference on Concept Lattices and Their Applications
CEUR Workshop Proceedings, 2018.
Similar publications
Formal Concept Analysis for Evaluating Intrinsic Dimension of a Natural Language
Kuznetsov S., Gromov V., Borodin N. et al., Lecture Notes in Computer Science 2023 P. 331–339
Some results of a computational experiment for determining the intrinsic dimension of linguistic varieties for the Bengali and Russian languages are presented. At the same time, both sets of words and sets of bigrams in these languages were considered separately. The method used to solve this problem was based on formal concept analysis algorithms. It ...
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
Intrinsically Interpretable Document Classification via Concept Lattices
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
On Suboptimality of GreConD for Boolean Matrix Factorisation of Contranominal Scales
Ignatov D. I., Yakovleva A., , in: Proceedings of the 9th International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2021)Vol. 2972.: CEUR-WS, 2021. P. 87–98.
In this paper we study certain properties of the GreConD algorithm for Boolean matrix factorisation, a popular technique in Data Mining with binary relational data. This greedy algorithm was inspired by the fact that the optimal number of factors for the Boolean matrix factorisation can be chosen among the formal concepts of the correspond- ing ...
Added: November 1, 2021
Topology of nerves and formal concepts
Ayzenberg A., / Series arXiv "math". 2019.
The general goal of this paper is to gather and review several methods from homotopy and combinatorial topology and formal concepts analysis (FCA) and analyze their connections. FCA appears naturally in the problem of combinatorial simplification of simplicial complexes and allows to see a certain duality on a class of simplicial complexes. This duality generalizes ...
Added: November 15, 2019
Supplementary Proceedings ICFCA 2019 Conference and Workshops
CEUR Workshop Proceedings, 2019.
Added: October 31, 2019
Numerical Pattern Mining Through Compression
Makhalova T., Kuznetsov S., Napoli A., , in: 2019 Data Compression Conference Proceedings.: IEEE, 2019. P. 112–121.
Pattern Mining (PM) has a prominent place in Data Science and finds its application in a wide range of domains. To avoid the exponential explosion of patterns different methods have been proposed. They are based on assumptions on interestingness and usually return very different pattern sets. In this paper, we propose to use a compression-based ...
Added: July 2, 2019
What Can Pareto Optimality Do for Clustering?
Makhalova T., , in: Искусственный интеллект в решении актуальных социальных и экономических проблем ххi века: сборник статей Всероссийской научно-практической конференции (14-18 мая 2018 г, г. Пермь).: ПГНИУ, 2018.
Cluster assessment remains one of the most actual problems in data mining. In this paper, a new approach to the selection of clusters based on a combination of measures of cluster quality is proposed. The new approach incorporates easily expert understanding of “interestingness” of clusters and does not require pre-defined parameters and thresholds. The subset ...
Added: November 25, 2018
MDL for FCA: is there a place for background knowledge?
Kuznetsov S., Makhalova T., Napoli A., , in: Proceedings of the International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at IJCAI/ECAI 2018)Vol. 2149: CEUR Workshop Proceedings.: CEUR-WS, 2018.
Added: September 6, 2018
On interestingness measures of formal concepts
Kuznetsov S., Makhalova T., Information Sciences 2018 No. 442–443 P. 202–219
Formal concepts and closed itemsets proved to be of big importance for knowledge discovery, both as a tool for concise representation of association rules and a tool for clustering and constructing domain taxonomies and ontologies. Exponential explosion makes it difficult to consider the whole concept lattice arising from data, one needs to select most useful ...
Added: March 22, 2018
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