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2012 | 10 | 4 | 1149-1156
Tytuł artykułu

Instance-based regression with missing data applied to a photocatalytic oxidation process

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, a modified nearest-neighbor regression method (kNN) is proposed to model a process with incomplete information of the measurements. This technique is based on the variation of the coefficients used to weight the distances of the instances. The case study selected for testing this algorithm was the photocatalytic degradation of Reactive Red 184 (RR184), a dye belonging to the group of azo compounds, which is widely used in manufacturing paint paper, leather and fabrics. The process is conducted with TiO2 as catalyst (an inexpensive semiconductor material, completely inert chemically and biologically), in the presence of H2O2 (with the role of increasing the rate of photo-oxidation), at different pH values. The final concentration of RR184 is predicted accurately with the modified kNN regression method developed in this article. A comparison with other machine learning methods (sequential minimal optimization regression, decision table, reduced error pruning tree, M5 pruned model tree) proves the superiority and efficiency of the proposed algorithm, not only for its results, but for its simplicity and flexibility in manipulating incomplete experimental data. [...]
Wydawca

Czasopismo
Rocznik
Tom
10
Numer
4
Strony
1149-1156
Opis fizyczny
Daty
wydano
2012-08-01
online
2012-05-29
Twórcy
autor
  • Department of Computer Science and Engineering, “Gheorghe Asachi” Technical University Iasi, 700050, Iasi, Romania
  • Department of Chemical Engineering, “Gheorghe Asachi” Technical University Iasi, 700050, Iasi, Romania
  • Department of Chemical Engineering, “Gheorghe Asachi” Technical University Iasi, 700050, Iasi, Romania, silvia_curteanu@yahoo.com
  • Department of Chemistry, Laboratory of Physical Chemistry, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
Bibliografia
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Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.-psjd-doi-10_2478_s11532-012-0038-x
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