The Modified Method of Logical Analysis Used for Solving Classification Problems

Kuzmich, Roman; Ступина, А. А.; Корпачева, Л. Н.; Ezhemanskaja, Svetlana; Rouiga, Irina

doi:10.15388/informatica.2018.176

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…Since the importance of correctly identifying positive class objects (true positive rate or sensitivity) is greater than a negative one (true negative rate or specificity), the best option is to select patterns with informativeness greater than 4, since the accuracy of classifying objects in the positive class is higher. The obtained classification accuracy exceeds the accuracy obtained in [44].…”

Section: Another Approach To Data Preparation (Reduced Sampling Witho...contrasting

confidence: 71%

“…However, these patterns should be of a sufficient level of quality, expressed in the requirements for the parameters of the pattern (for example, complexity or degree). To implement this stage, a specific criterion is selected as well as an optimization algorithm for constructing patterns that is relevant to the data under consideration [44].…”

Section: Main Stages Of Logical Analysis Of Datamentioning

confidence: 99%

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Formation of Fuzzy Patterns in Logical Analysis of Data Using a Multi-Criteria Genetic Algorithm

Masich

Kulachenko²,

Stanimirović

et al. 2022

Symmetry

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The formation of patterns is one of the main stages in logical data analysis. Fuzzy approaches to pattern generation in logical analysis of data allow the pattern to cover not only objects of the target class, but also a certain proportion of objects of the opposite class. In this case, pattern search is an optimization problem with the maximum coverage of the target class as an objective function, and some allowed coverage of the opposite class as a constraint. We propose a more flexible and symmetric optimization model which does not impose a strict restriction on the pattern coverage of the opposite class observations. Instead, our model converts such a restriction (purity restriction) into an additional criterion. Both, coverage of the target class and the opposite class are two objective functions of the optimization problem. The search for a balance of these criteria is the essence of the proposed optimization method. We propose a modified evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to solve this problem. The new algorithm uses pattern formation as an approximation of the Pareto set and considers the solution’s representation in logical analysis of data and the informativeness of patterns. We have tested our approach on two applied medical problems of classification under conditions of sample asymmetry: one class significantly dominated the other. The classification results were comparable and, in some cases, better than the results of commonly used machine learning algorithms in terms of accuracy, without losing the interpretability.

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Section: Another Approach To Data Preparation (Reduced Sampling Witho...contrasting

confidence: 71%

Section: Main Stages Of Logical Analysis Of Datamentioning

confidence: 99%

Formation of Fuzzy Patterns in Logical Analysis of Data Using a Multi-Criteria Genetic Algorithm

Masich

Kulachenko²,

Stanimirović

et al. 2022

Symmetry

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show abstract

“…Each algorithm has its own advantages and disadvantages. In this paper, we will consider a general problem associated with the operation https://doi.org/10.1051/itmconf/20245903009 HMMOCS-II 2023 of most machine learning methods, using the example of the method of logical analysis of data [5].…”

Section: Introductionmentioning

confidence: 99%

Influence of the number of attribute binarization thresholds on the quality of the generated set of rules for the method of logical analysis of data

Roman,

Katerina,

Artur

2024

ITM Web Conf.

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For efficient operation of machine learning methods, it is necessary to set their parameters up properly. Both the computational complexity and the accuracy of the method for the problem being solved can depend on the selected parameter values. The paper discusses the method of logical analysis of data, which is used to solve classification problems and consists of a number of steps. At each step, it is necessary to set the method parameters up to suit the problem being solved. When setting parameters, one should be guided by a compromise between the accuracy of the method and the computational complexity. With comparable values for classification accuracy of several variants of the method implementation, preference will always be given to the simplest of them in terms of computational complexity. Since the method works only with binary characteristics, at the first stage it is necessary to binarize quantitative characteristics. The binarization procedure is associated with the choice of the “number of binarization thresholds” parameter. This paper proposes an experimental approach to determine the best value of the specified method parameter for the problem being solved.

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“…This requires the selection of those features with combinatorial effects [18,19]. It is also important to select the most informative and significant patterns [20,21] and to form a LAD model (decision rule) [22].…”

Section: Introductionmentioning

confidence: 99%

Paired Patterns in Logical Analysis of Data for Decision Support in Recognition

Masich

Tynchenko²,

Nelyub³

et al. 2022

Computation

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Logical analysis of data (LAD), an approach to data analysis based on Boolean functions, combinatorics, and optimization, can be considered one of the methods of interpretable machine learning. A feature of LAD is that, among many patterns, different types of patterns can be identified, for example, prime, strong, spanned, and maximum. This paper proposes a decision-support approach to recognition by sharing different types of patterns to improve the quality of recognition in terms of accuracy, interpretability, and validity. An algorithm was developed to search for pairs of strong patterns (prime and spanned) with the same coverage as the training sample, having the smallest (for the prime pattern) and the largest (for the spanned pattern) number of conditions. The proposed approach leads to a decrease in the number of unrecognized observations (compared with the use of spanned patterns only) by 1.5–2 times (experimental results), to some reduction in recognition errors (compared with the use of prime patterns only) of approximately 1% (depending on the dataset) and makes it possible to assess in more detail the level of confidence of the recognition result due to a refined decision-making scheme that uses the information about the number and type of patterns covering the observation.

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The Modified Method of Logical Analysis Used for Solving Classification Problems

Cited by 6 publications

References 9 publications

Formation of Fuzzy Patterns in Logical Analysis of Data Using a Multi-Criteria Genetic Algorithm

Formation of Fuzzy Patterns in Logical Analysis of Data Using a Multi-Criteria Genetic Algorithm

Influence of the number of attribute binarization thresholds on the quality of the generated set of rules for the method of logical analysis of data

Paired Patterns in Logical Analysis of Data for Decision Support in Recognition

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