Classification of Noisy Data: An Approach Based on Genetic Algorithms and Voronoi Tessellation

Abdul Rauf Khan, Henrik Schiøler, Torben Knudsen, Murat Kulahci, Mohamed Zaki

Publikation: Working paperForskning

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Resumé

Classification is one of the major constituents of the data-mining toolkit. The
well-known methods for classification are built on either the principle of
logic or statistical/mathematical reasoning for classification. In this article we
propose: (1) a different strategy, which is based on the portioning of
information space; and (2) use of the genetic algorithm to solve
combinatorial problems for classification. In particular, we will implement
our methodology to solve complex classification problems and compare the
performance of our classifier with other well-known methods (SVM, KNN,
and ANN). The results of this study suggest that our proposed methodology
is specialized to deal with the classification problem of highly imbalanced
classes with significant overlap.
OriginalsprogEngelsk
UdgiverCambridge University Network
Antal sider10
StatusUdgivet - nov. 2016

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genetic algorithm
data mining
methodology
method

Citer dette

Khan, Abdul Rauf ; Schiøler, Henrik ; Knudsen, Torben ; Kulahci, Murat ; Zaki, Mohamed . / Classification of Noisy Data: An Approach Based on Genetic Algorithms and Voronoi Tessellation. Cambridge University Network, 2016.
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abstract = "Classification is one of the major constituents of the data-mining toolkit. Thewell-known methods for classification are built on either the principle oflogic or statistical/mathematical reasoning for classification. In this article wepropose: (1) a different strategy, which is based on the portioning ofinformation space; and (2) use of the genetic algorithm to solvecombinatorial problems for classification. In particular, we will implementour methodology to solve complex classification problems and compare theperformance of our classifier with other well-known methods (SVM, KNN,and ANN). The results of this study suggest that our proposed methodologyis specialized to deal with the classification problem of highly imbalancedclasses with significant overlap.",
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Classification of Noisy Data: An Approach Based on Genetic Algorithms and Voronoi Tessellation. / Khan, Abdul Rauf; Schiøler, Henrik; Knudsen, Torben; Kulahci, Murat; Zaki, Mohamed .

Cambridge University Network, 2016.

Publikation: Working paperForskning

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T1 - Classification of Noisy Data: An Approach Based on Genetic Algorithms and Voronoi Tessellation

AU - Khan, Abdul Rauf

AU - Schiøler, Henrik

AU - Knudsen, Torben

AU - Kulahci, Murat

AU - Zaki, Mohamed

PY - 2016/11

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AB - Classification is one of the major constituents of the data-mining toolkit. Thewell-known methods for classification are built on either the principle oflogic or statistical/mathematical reasoning for classification. In this article wepropose: (1) a different strategy, which is based on the portioning ofinformation space; and (2) use of the genetic algorithm to solvecombinatorial problems for classification. In particular, we will implementour methodology to solve complex classification problems and compare theperformance of our classifier with other well-known methods (SVM, KNN,and ANN). The results of this study suggest that our proposed methodologyis specialized to deal with the classification problem of highly imbalancedclasses with significant overlap.

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PB - Cambridge University Network

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