Resolving the Conflicts Between Cuts in a Decision Tree with Verifying Cuts

Buregwa-Czuma, Sylwia; Bazan, Jan G.; Bazan-Socha, Stanisława; Rząsa, Wojciech; Dydo, Lukasz; Skowron, Andrzej

doi:10.1007/978-3-319-60840-2_30

Cited by 3 publications

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“…Classifying algorithms are applied to solve problems in machine learning, pattern recognition, expert systems, knowledge discovery, and data mining 1 . There are several ways to construct classifiers, which rely on decision rules, decision trees, neural networks, inductive logic programming, and classical or modern statistical methods 1–5 . Data sets used in classifiers may be presented by data tables where objects correspond to rows and attributes correspond to columns of the given data table.…”

Section: Introductionmentioning

confidence: 99%

“…1 There are several ways to construct classifiers, which rely on decision rules, decision trees, neural networks, inductive logic programming, and classical or modern statistical methods. [1][2][3][4][5] Data sets used in classifiers may be presented by data tables where objects correspond to rows and attributes correspond to columns of the given data table . In this contribution we consider decision tables of the form U A d T = ( , , ), 6 where A is the set of attributes, U is the set of objects, and d is a decision attribute (distinguished from the set A).…”

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confidence: 99%

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Interval modelling in optimization of k‐NN classifiers for large number of attributes in data sets on an example of DNA microarrays

Bentkowska

Bazan

Zaręba

et al. 2021

Int J of Intelligent Sys

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In this contribution there are considered interval‐valued methods of improving the quality of classification by the k‐ N N binary classifiers in the case of large number of attributes in data sets. One of the possible applications of the introduced method are the microarray data so the presented results may be applied in medical diagnosis support, for example, in identification of marker genes. The proposed algorithm involving reduction methods of time complexity and interval modeling of data using interval‐valued aggregation functions has a significantly higher classification quality than the other considered algorithm with the aggregation method involving the numerical arithmetic mean. The former algorithm is based on the interval‐valued aggregation of uncertainty intervals determined on the basis of certainty coefficients of individual k‐ N N classifiers while the latter is based on the aggregation of certainty coefficients of individual k‐ N N classifiers with the use of the numerical arithmetic mean. Moreover, the performance of the new algorithm based on interval‐valued methods was compared with the known from the literature classifiers in cancer diagnosis based on gene expression microarrays. The obtained results prove that in practical applications the newly proposed algorithm can be successfully used.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%