Interface between Logical Analysis of Data and Formal Concept Analysis

Janostik, Radek; Konečný, Jan; Krajča, Petr

doi:10.1016/j.ejor.2020.01.015

Cited by 14 publications

(3 citation statements)

References 44 publications

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“…Jocelyna et al [32] use LAD for the prevention of accidents caused by belt conveyors. LAD also has applications in computer science [33,34]. Leujune et al [29] and Ouyang & Chou [35] also report recent developments in the theory of LAD.…”

Section: Logical Analysis Of Data Logical Analysis Of Data (Lad)mentioning

confidence: 99%

Reconstruction Rating Model of Sovereign Debt by Logical Analysis of Data

Gholipour

Vízvári

Lakner

2021

Mathematical Problems in Engineering

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Sovereign debt ratings provided by rating agencies measure the solvency of a country, as gauged by a lender or an investor. It is an indication of the risk involved in investment and should be determined correctly and in a well-timed manner. The current system is lacking transparency of rating criteria and mechanism. The present study reconstructs sovereign debt ratings through logical analysis of data (LAD), which is based on the theory of Boolean functions. It organizes groups of countries according to 20 World Bank-defined variables for the period 2012–2015. The Fitch Rating Agency, one of the three big global rating agencies, is used as a case study. An approximate algorithm was crucial in exploring the rating method, in correcting the agency’s errors, and in determining the estimated rating of otherwise unrated countries. The outcome was a decision tree for each year. Each country was assigned a rating. On average, the algorithm reached almost 98% matched ratings in the training set and was verified by 84% in the test set.

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Section: Logical Analysis Of Data Logical Analysis Of Data (Lad)mentioning

confidence: 99%

Reconstruction Rating Model of Sovereign Debt by Logical Analysis of Data

Gholipour

Vízvári

Lakner

2021

Mathematical Problems in Engineering

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“…Ref. [26] describes the relationship between formal FCA concepts and LAD models based on the equivalence of their basic building blocks. As a potential advantage of this relationship, the authors suggest using FCA algorithms to compute LAD patterns.…”

Section: Introductionmentioning

confidence: 99%

Subgroup Discovery in Machine Learning Problems with Formal Concepts Analysis and Test Theory Algorithms

et al. 2023

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A number of real-world problems of automatic grouping of objects or clustering require a reasonable solution and the possibility of interpreting the result. More specific is the problem of identifying homogeneous subgroups of objects. The number of groups in such a dataset is not specified, and it is required to justify and describe the proposed grouping model. As a tool for interpretable machine learning, we consider formal concept analysis (FCA). To reduce the problem with real attributes to a problem that allows the use of FCA, we use the search for the optimal number and location of cut points and the optimization of the support set of attributes. The approach to identifying homogeneous subgroups was tested on tasks for which interpretability is important: the problem of clustering industrial products according to primary tests (for example, transistors, diodes, and microcircuits) as well as gene expression data (collected to solve the problem of predicting cancerous tumors). For the data under consideration, logical concepts are identified, formed in the form of a lattice of formal concepts. Revealed concepts are evaluated according to indicators of informativeness and can be considered as homogeneous subgroups of elements and their indicative descriptions. The proposed approach makes it possible to single out homogeneous subgroups of elements and provides a description of their characteristics, which can be considered as tougher norms that the elements of the subgroup satisfy. A comparison is made with the COBWEB algorithm designed for conceptual clustering of objects. This algorithm is aimed at discovering probabilistic concepts. The resulting lattices of logical concepts and probabilistic concepts for the considered datasets are simple and easy to interpret.

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“…In details, one can remind some aspects of FCA in data mining and machine learning (see [22], [25], [46] and references therein):…”

Section: Introductionmentioning

confidence: 99%

Graded Galois Lattices and Closed Itemsets

Sotoudeh¹,

Goudarzi²,

Nikoukar³

2021

Preprint

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The Galois lattice is a graphic method of representing knowledge structures. The first basic purpose in this paper is to introduce a new class of Galois lattices, called graded Galois lattices. As a direct result, one can obtain the notion of graded closed itemsets (sets of items), to extend the definition of closed itemsets. Our second important goal in this paper, is related to set a constructive method, computing the graded formal concepts and graded closed itemsets. We mean by a constructive method, a method that builds up a complete solution from scratch by sequentially adding components to a partial solution until the solution is complete. Besides of computational aspects, our methods in this paper are based on the strong results obtained by special mappings in the realm of domain theory. To reach the fertilized consequences and constructive algorithms, we need to push the study to the structures of Banach lattices.

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Interface between Logical Analysis of Data and Formal Concept Analysis

Cited by 14 publications

References 44 publications

Reconstruction Rating Model of Sovereign Debt by Logical Analysis of Data

Reconstruction Rating Model of Sovereign Debt by Logical Analysis of Data

Subgroup Discovery in Machine Learning Problems with Formal Concepts Analysis and Test Theory Algorithms

Graded Galois Lattices and Closed Itemsets

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